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    2019 Summer Internship Program: So much more than coffee

    enJuly 02, 2019
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    About this Episode

    In this episode of Beneath the Subsurface we introduce our Geoscience and Data & Analytics intern teams for our summer internship program. Erica kicks off the episode with Jason and Sri talking about how the programs have come about and changed overtime here at TGS, how they select and recruit for the program, and the scope of the projects that the internships tackle this summer. Erica then spends time with both teams of interns discussing the experience in the program, what they’ve learned, and everything they’ll be taking away and applying back to their studies and upcoming careers.

     

    TABLE OF CONTENTS
    00:00 - Intro
    00:50 - Team Leader Segment with Jason and Sri
    01:09 - The Geoscience Internship Program
    04:42 - The Data & Analytics Internship Program
    07:29 - Advice for Program Applicants
    11:54 - Data & Analytics Intern Team Introductions
    13:32 - The D&A Summer Projects
    15:18 - Lessons Learned Pt. 1
    17:20 - The TGS Internship Experience Pt. 1
    20:24 - Future Careers
    21:41 - Advice for Future Interns & Reasons to Apply Pt. 1
    24:34 - Valuable Take Aways Pt. 1
    26:01 - Geoscience Intern Team Introductions
    28:36 - The Geoscience Summer Projects
    31:33 - Lessons Learned Pt. 2
    33:14 - The TGS Internship Experience Pt. 2
    34:12 - Advice for Future Interns & Reasons to Apply Pt. 2
    39:28 - Valuable Take Aways Pt. 2

    EXPLORE MORE FROM THE EPISODE

    EPISODE TRANSCRIPT
    Erica Conedera:
    00:12
    Hello and welcome to Beneath the Subsurface a podcast that explores the intersection of geoscience and technology. From the Software Development Department here at TGS, I'm your host, Erica Conedera. This time around, we'll be chatting with our newest batch of intrepid students in TGS' dynamic and immersive internship program. As you will hear, they are a diverse group of future innovators from around the world. They bring with them a wide range of skills and interests and work together to collaborate on exciting real world projects. We'll start our conversation today with a quick introduction from the leaders of our internship program. I'm here with Sri Kainkarayam, the data science lead and Jason Kegel with the geoscience team who heads up the geoscience intern program. And we're going to talk a little bit about the internship programs. Jason, how has this program changed in the last five years?

    Jason Kegel:01:09
    When we first started the program, I want to say 2013, 2014, it was out of the Calgary office in Canada. The interns there were mainly from some of our Calgary schools nearby. And then it started to grow 2014, 2015 to include some of our Texas schools, UT, Baylor, University of Houston. As it's grown, we've decided to add more projects and more sort of interesting work to the projects. We've also been able to bring on some of our original interns into roles within the company. So over the last five years, I'd say the biggest thing that's grown is the, the number of interns. So in Calgary, when this first started we had one intern and then that same intern came back a second year and we brought another one on. And then we got one in Houston. And then as that grew, we had a couple in Houston and a couple in Calgary.

    Jason: 02:09
    And then the past couple of years we've had four each year. So we had four last year and four this year. So we've really been able to sort of guide new projects around that to where we can really include their schoolwork and what they're doing in their university work with what we're doing here at TGS and hopefully build a sort of cohesive project for them to work on. And that's sort of the struggle with a lot of internship projects that we've done over the past years is to incorporate what they want to do as students and as interns and as their career grows, with what we'd like to see them do and encourage them to do within TGS.

    Erica:
    02:49
    Does that go into the consideration of which interns you end up picking, what their specialties are or what they're looking to do with what you need?

    Jason:
    02:58
    No, not necessarily, a lot of the times the interns, so for example, last year we were working very closely with a couple of schools that we wanted to bring data into. So some of our production data our Longbow group into with the University of Lafayette. So we were working really closely with a few professors out of that school and a few professors with UH. So we had recommendations from the professors themselves with students that they thought might work nicely with us with - in terms of their knowledge of data already and their knowledge of well log use and seismic, so they can kind of jump in running without having to learn too much in the beginning, without too much of a learning curve. So in aspects of that, and that's, that's more that we look for. So the, the professors we're working with, along with how long it will take them to, to get up and running with things.

    Jason:
    03:51
    Our current group of students is sort of a more advanced set of students who are working on their PhDs or in their later years of their master's degrees. So they've already seen a lot of these areas and worked with a lot of the data. So we do look for sort of more advanced students now, whereas when we first started the program, we were, we were happy to get anybody, some people that were not sure if they were going to be geoscientists, but you know, we're in the geoscience program with their bachelor's and that was okay too. I think we still got a lot out of having them here, working with us. but as we've grown, we've been putting them on more and more advanced projects and they've really been able to help out.

    Erica:
    04:29
    Cool, sounds like they've added a lot of value.

    Jason:
    04:30
    They definitely do. And it's nice to have sort of fresh faces around in the summertime and, and it really, really fills in for everybody that goes on vacation in the summer.

    Erica:
    04:39
    (Laughter) Right? Awesome.

    Jason:
    04:39
    The office doesn't seem so empty.

    Erica:
    04:42
    Awesome. So for the data analytics team, the internship program is new. I think this is your first batch of interns, correct Sri?

    Sri Kainkaryam:
    04:57
    Yes. So the data science team started sometime around November, 2017 so this is, although this has been our second summer, this is our first batch of interns that are projects, both, trying to test out novel algorithms, novel approaches, also try and apply ideas from high performance computing to building workflows, and also try and build sort of, user interfaces or ability to, deploy these for various users. So, there are broadly three buckets in which these projects fall into. And, it's an, it's, it was an interesting time looking for an intern because data science as, as a domain is, sits at the intersection of sort of three, broadly non intersecting sets, right? So geoscience, computing as well as machine learning or deep learning and folks having adequate background in all three of them, they sort of fit the -the mold of a good intern.

    Sri:
    06:02
    So it was in some sense was a little hard initially to try and find an intern. So I think we have a talented group of interns working on two of the broad offerings that we have right now. One of them is Salt Net, that is trying to interpret salt bodies from seismic images, and one is called ARLAS that is curve completion and aspects of petrophysics that can be done on, on wells that are available in an entire basin. So, it's, it's been four weeks into the internship program and the interns, the interns are pretty smart. They're motivated and it's been a fun experience so far.

    Erica:
    06:43
    Is it a 12 week program in total?

    Sri:
    06:46
    It's around a 12 week program. Some of them I think are here for a little longer than that. So, one of them is, trying to build a tensorflow port of our salt network flow because tensorflow community comes with a bunch of advantages such as, like, ability to deploy, it also comes with a JavaScript library called tensorflow JS that that makes it easy to do machine learning in the browser. So we want to make use of that infrastructure and the community built infrastructure. And that's one of the reasons why, one of the interns is spending time trying to build, trying to put our workflow in onto tensorflow.

    Erica:
    07:29
    So if you guys had some advice to give to people looking to get into the internship program, would you have anything you'd want to let them know?

    Sri:
    07:37
    So from the perspective of data science internships, given that how fast the field is moving, especially for students looking for data science internships in, in the space of oil and gas, the first and foremost thing is having an ability to understand various aspects, various various sources of data or aspects of data in the upstream domain. Because, just to give you an example, somebody who's worked on deep learning of natural images throughout, the moment you try and apply similar algorithms onto seismic images, it's a completely different domain. So, what are the, what are some of the assumptions that you can make? And that's where having a strong domain background really helps.

    Sri:
    08:30
    And I think the second thing that is, that's becoming very important in the marketplace right now is, is with, with platforms like GitHub or, you know, various open source projects. You can actually showcase your code. So pick a problem, learn a few, learn some approaches or try out some novel approaches, and put out the code out there. Put that on your resume because that adds a lot of weight, in your, in your ability to make a case for an internship rather than somebody who hasn't, who says, oh, I have, I have a strong programming background, but there's no way for somebody who's evaluating the person to see the code. So that these days has become a really strong advantage for, for a lot of students. So a couple of the students that are working with us this summer, they actually have active GitHub profiles where they've posted code, they've contributed code, various projects and so on. And as a consequence, like we looked at their profiles and backgrounds and like, oh, this is an obvious fit to our group and this person also has a background. A couple of them were like Ph.D students in geophysics, so it's an obvious fit for our team. So it was, it was all, it was a no-brainer for us to get them to come work with us this summer,

    Erica:
    09:53
    Jason?

    Jason:
    09:53
    On the geoscience side, it's, it's quite a bit different really. A lot of the students that are in university going for, for geoscience and wanting to go into the oil and gas industry have mainly just academic experience. So we really just want somebody that can sort of get up to speed quickly with sort of what an explorationist in an oil and gas company would do is look at essentially what we're bringing them in to do is what a sort of a mini, really quick exploration studies on basins where they don't have to go full on to drill a well, but they still need to have the ideas behind it where they can use the data, they have to evaluate an area and come up to speed quickly with, with getting those presentations out. So having really good presentation skills and having just a background enough to be able to learn on their own and pick up concepts quickly really helps. We see that a lot with, since we do get a lot of our interns through their advisors at different universities, that that really helps. But it also doesn't hinder it. We've also had lots of students that have applied, that have came from different universities where we don't know the advisors and it's just a matter of them going through the interview process and showcasing that they're, they're able to get to speed quickly. So, anybody can really go, go and do this type of work if they have the, the ability to learn.

    Erica:
    11:14
    Awesome.

    Sri:
    11:14
    I think that's an interesting point that Jason brought up. The ability to learn things fast and, sort of the ability to, appreciate various data sets and trying to understand and bring them together. I think that's a huge advantage for, for students. And based on my interaction with students in our group as well as Jason's group, I think TGS this summer has a fabulous group of interns.

    Erica:
    11:43
    Okay. Well thank you guys for talking to us about the internship program and we're very happy to talk to your respective groups and see what they have to say. Thank you.

    Sri:
    11:52
    Thank very much.

    Jason:
    11:53
    Thank you.

    Erica:
    11:56
    I'm sitting here with our first group of interns from the data and analytics group. To my left, we have Michael Turek from Florida State University. His major is computer science. He has a B.S. In computer science as an Undergrad. What are your career goals? What are you working towards?

    Michael Turek:
    12:15
    Yes. So part of me taking an internship here at TGS was to help figure that out. And so, well, you know, my interests rely mostly in machine learning and things like this. So something pretty, along those lines.

    Erica:
    12:31
    Awesome. Well we hope you, we'll help you figure that out. While you're here. Going around the table, we have Lingxiao Jia from the University of Wyoming. Your major is geophysics and you're working towards your PhD studying seismic imaging, migration and inversion. What kind of career are you working towards?

    Lingxiao Jia:
    12:50
    I plan to work as a Geoscientist in the oil and gas industry.

    Erica:
    12:56
    Awesome.

    Lingxiao:
    12:56
    Yeah, I like to do programming, so mostly on that.

    Erica:
    13:06
    Cool. All right. And then to my right, we had Deepthi Sen, from Texas A&M, majoring in petroleum engineering, working towards your PhD, studying reservoir engineering. What's your career goal, Ms. Deepthi?

    Deepthi Sen:
    13:21
    I'd like to, get a full time employment in the oil industry, preferably working on something related to machine learning in reservoir engineering. So yeah, that's why one of the reasons why I'm here too.

    Erica:
    13:33
    Awesome. Yeah. Oh, we're glad all of you are here. So can you guys describe for us, the projects you're working on? I'm not sure if you guys are all working on the same project or if you're working on different projects.

    Deepthi:
    13:45
    We are working on different projects. So right now I'm working on something which, involves clustering well logs, into good and bad, sections.

    Deepthi:
    13:57
    I use machine learning and a few algorithms that I use for my graduate research too.

    Erica:
    14:04
    Very cool. What's a bad section?

    Deepthi:
    14:07
    A bad section as in, there are certain depths at which, certain well logs behave erratically so we want, do not want to use that data, so we have to cluster it out. So, in order to do that manually for, you know, thousands of wells, it's impossible. So that's where machine learning comes into play.

    Erica:
    14:27
    Very cool. Very useful too. Lingxiao?

    Lingxiao:
    14:32
    I'll be working on using machine learning to do the recognition of geoscience features. For example, there could be faults, it could be picking horizons, could be recognizing salt domes, something like that.

    Erica:
    14:48
    Wow. Very complex and over my head. (Laughter) I'm sure it's very important though. And you, sir?

    Michael:
    14:57
    Yeah, so I'm working on translating the models that TGS' data analytics team uses to predict salt patches in the earth. So they use, they use models written in a module called Pi Torch and I'm converting that to tensorflow 2.0

    Erica:
    15:17
    Cool. Very cool. So what have you guys learned along the way so far? I know this is kind of the beginning for you, but-

    Michael:
    15:28
    Yeah, so it's, it's somewhat difficult to- so much, is kind of the answer to that question. But a lot of what I've learned boils down to more of the theory side of machine learning. Coming into the internship I didn't know a whole lot about the backend of machine learning, mostly just applying it. So learning how all these models work and why they work and things like that in terms of, the actual actually applying machine learning. That's what I've learned. I've also learned though, perhaps more importantly, working with a team and collaborating and things like that, which has been-

    Erica:
    16:10
    So hands on, real-world experience. What do you guys say to that? Ladies, I should say (Laughter) to my right.

    Deepthi:
    16:17
    So as I said, the research that I do is again, on machine learning. So I get to use similar algorithms to another, I would say facet of oil and gas. So I worked in reservoir engineering back in Grad school. Here I'm working on, petrophysics, so I kind of see how the same algorithms and same concepts can be applied in two different, areas, which is quite eye opening. Yeah. And apart from that I'm learning new algorithms and learning new math, which, I would think that's very important for, for my Grad school too, so, one good thing about TGS is that, they are quite, you know, they don't mind, publishing. So as a PhD student, that's very important to me. So that's one thing I look forward to too.

    Erica:
    17:08
    Yeah. Awesome.

    Lingxiao:
    17:10
    For me, it has helped me get a deeper understanding of how much, how machine learning works and how it could be applied to the field of Geo Sciences.

    Erica:
    17:20
    Cool. So talking about TGS more broadly, like as a culture, how would you say it's like working here, if someone were to ask you from school, what's it like working at TGS? What's that company like? What would you say?

    Deephti:
    17:36
    It's a very friendly atmosphere and, it is different from Grad School, in the sense that, I think Grad School, hours are more flexible than in an industry environment. But then, the focus is different and this is more, you know, I would think this more social than Grad school and, you know, being here, this is my first internship in the US, the environment is very friendly and you know, people look out for each other it's great.

    Erica:
    18:15
    Cool.

    Lingxiao:
    18:15
    Yeah. People here are so helpful and the, I have had a great time. I really enjoy this internship by far. Yeah.

    Erica:
    18:26
    Awesome.

    Michael:
    18:26
    It's wonderful. You're working in small teams and so you get to know everyone pretty well. It's very tight knit and those people are smart and very helpful kind people. It's, it's, it's wonderful.

    Erica:
    18:37
    Cool. Any surprises along the way? Anything you weren't expecting?

    Michael:
    18:44
    So, no, I wouldn't say there's anything that surprised me. I mean apart from the environment I had a much more perhaps rigid definition of, you know, you go to work and do your job and that's kind of that, but it's much more relaxed and that was, I guess, somewhat surprising.

    Erica:
    19:01
    Okay. I like that. Yeah. How bad the drive was maybe?

    Deepthi:
    19:06
    Yeah, I stay close by.

    Erica:
    19:09
    That's good. That's the way to do it. (Laughter) Yeah. What are you guys looking forward to for the remainder of your internships?

    Michael:
    19:17
    Yeah, so I'm looking forward since I'm rewriting these, these models and an interface for them, it'll be exciting to see them, how they perform and also to actually see the data and analytics team using them and hopefully finding them useful.

    Erica:
    19:31
    Yeah to see value for what you're working on. Absolutely.

    Deepthi:
    19:34
    So I'm about to finish the first part of my project, so I would like to wrap it up, you know, produce some good results and maybe get a publication out of it. And after that, yeah, I have a plan for what is to be done next, regarding the same, using the same similar approach but in a different setting. Yeah. So I'm looking forward to that.

    Erica:
    19:59
    Can you tell us what the different setting is or is that classified?

    Deepthi:
    20:03
    I'm not sure. (Laughter)

    Erica:
    20:05
    Right. We'll leave that one alone.

    Lingxiao:
    20:08
    So doing an internship here at TGS is an amazing adventure. I learn and discover new things everyday and I feel time passes very quickly, and everything is moving at a timely manner. So it's pretty good.

    Erica:
    20:24
    Nice. So I think we kind of touched upon how you guys are going to apply what you've learned here, at your careers as you go forward. Is there any particular job title that you guys think you're going to go towards?

    Deepthi:
    20:44
    Yeah. I probably will be going for a data scientist role, or I can say because of my background in reservoir engineering, I can go both on the data and science roles or the reservoir engineering roles. But yeah, from my experience here, I would, I think I would prefer to go to the data and data science roles because, there are like lots of opportunities out there and, the experience that I've gained here, I, I think it's going to be very helpful finding a full time position later on. Yeah.

    Lingxiao:
    21:18
    I could consider becoming a Geoscientist in the oil and gas or becoming a structural engineer because I have a programming background.

    Michael:
    21:32
    Yeah. I wouldn't say I have any career title I'm, I'm seeking out, but perhaps data scientist, but I'm not sure.

    Erica:
    21:41
    So what advice would you give to the interns who are going to be coming behind you?

    Michael:
    21:46
    Yeah. So probably to just build strong relationships with the team that you're in. Learn as much as you can, as deeply as you can.

    Deepthi:
    21:58
    Yeah. I would suggest that before coming in, you can go through, or if they have a set plan for you. In my case they did. So I had read up and you know, known what I'm going to work on so you can, you know, straight away start working on the project you have a rather than, you know, spend a lot of time, reading up those things that can happen before you start the internship. And yes, once you're here, it's, very important to like keep in touch, you know, meet the mentors every day or you know, update them so you have a clear path that you need to, yeah.

    Erica:
    22:44
    Lingxiao?

    Lingxiao:
    22:44
    I would suggest to go talk with people and you see what everyone is working on.

    Erica:
    22:51
    So learn, learn what other people are doing as well.

    Lingxiao:
    22:55
    Yeah.

    Erica:
    22:55
    That, yeah, that makes good sense. So why did you guys apply for the internships here?

    Michael:
    23:05
    So I applied, cause I was just looking for an internship and I had heard that, well I had heard that, (Laughter)

    Erica:
    23:14
    Honest.

    Michael:
    23:14
    (Laughter) I had heard good reviews from people who I respect and and I knew that they had a new data and analytics team doing machine learning, doing things with machine learning. That piqued my interest. And so I told them I was interested.

    Erica:
    23:28
    So kind of diverge off of that. So what programs are you guys using? Like actual hands on programs?

    Michael:
    23:36
    Yeah. So, programs for me are pretty, pretty simple. I use, a coding ID, visual Studio Code, and an Internet browser.

    Erica:
    23:43
    Whoa, okay.

    Michael:
    23:46
    I do that to do my work.

    Erica:
    23:47
    Google and a calculator, alright.

    Michael:
    23:49
    Yeah, pretty much.

    Erica:
    23:52
    Deepthi?

    Deepthi:
    23:52
    Uh, what was the question again?

    Erica:
    23:56
    What programs do you guys use?

    Deepthi:
    23:59
    Again, I guess we are in the process of making a program, so what I use is just Jupyter, it's very basic.

    Erica:
    23:59
    It's built on Python correct?

    Deepthi:
    23:59
    Yes, it is Python, I use Jupyter ID, and I'm in the process of making something useful from scratch.

    Erica:
    24:22
    So lastly, would you guys recommend a TGS internship to your fellow students?

    All:
    24:27
    Yes, definitely. Yes. Yes, yes. Yeah. Awesome. Yes.

    Erica:
    24:34
    Okay. So open question to the table. What are you going to take back to your program that you learned from your internship here? Starting with Michael to the left?

    Michael:
    24:42
    Yeah, so I'm learning a lot about machine learning and so in computer science that's obviously going to be a direct parallel. I can take that back. But I really think that what I'm learning most here that I'll take back is just how to collaborate with people, how to talk with people in a team and work in that way. I think that'll -

    Erica:
    25:05
    Life skills.

    Michael:
    25:11
    Yes.

    Erica:
    25:11
    Lingxiao?

    Lingxiao:
    25:11
    So, since machine learning in such a hot topic. Now, the work that I did here could be really extended into a project in my PhD research. So, yeah I'm currently working on that.

    Erica:
    25:28
    Awesome. Deepthi?

    Deepthi:
    25:29
    So right now we're working on a clustering of time series data. So my, one of the projects that I'm working, at my Grad school is also on time series data, and I think I might be able to, you know, use the insights that I gained from, from TGS, directly to my, research. So that's something that I'm looking forward to.

    Erica:
    25:52
    Awesome. Okay, well thank you guys for talking with us today and I guess we'll let you get back to work now.

    Michael:
    25:59
    Thank you for having us.

    Deepthi:
    26:00
    Thank you.

    Lingxiao:
    26:01
    Thank you.

    Erica:
    26:01
    And now our last group for this episode, the geoscience interns.

    Erica:
    26:08
    Going around the table clockwise, we have Sean Romito. You're from the University of Houston, majoring in geology. You are working towards your PhD and you are studying magnetic basement structure of the Caribbean plate, tectonostratigraphy of South Gabon and Camamu-Almada conjugate basins. I totally know what all of that means. What career are you working towards?

    Sean Romito:
    26:35
    Oh, hello. Thank you for having me. Definitely exploration Geoscientist, this is kind of where I've been propelling my career, ever since I started with a bachelor's and I've just kinda been stepping towards that goal.

    Erica:
    26:51
    Awesome. All right. Now we have Geoff Jackson from the University of Louisiana at Lafayette Majoring in petroleum geology. Your program is a master's degree and you graduated last spring. Congratulations!

    Geoff Jackson:
    27:07
    Thank you!

    Erica:
    27:07
    You studied a prospect lead off of a salt dome in southern Louisiana, and you cannot give us any more details than that.

    Geoff:
    27:14
    Unfortunately yes.

    Erica:
    27:14
    Very mysterious. So what, what are your career goals?

    Geoff:
    27:19
    Uh, similar to Sean's I was going to say, I can probably speak for the group here, but we're all just trying to be geologists and getting on with an operator, going to say probably best case scenario.

    Erica:
    27:28
    Awesome. Next we have Hualing Zhang, from the University of Houston, majoring in geology, working towards a PhD. And you're studying structural analysis and gravity modeling in the Permian Basin in West Texas. And you are originally from Urumqi, Northwest China and you got interested in geology about traveling around. That is so cool. So is your career goal the same?

    Hualing:
    27:53
    Yeah, basically similar, I'm working towards a career goal in the oil industry. Yeah. Since, like, my dad is also a geologist. Yeah. He works in PetroChina. So yeah, that's also my career goal.

    Erica:
    28:08
    Awesome. Yeah. Awesome. All right. And lastly, Cahill Kelleghan from Colorado School of Mines, majoring in geology. You're working towards a Masters of science and geology, and you're studying sedimentology and basin analysis / modeling with your thesis being in the Delaware Basin. So career goals?

    Cahill:
    28:28
    I'm pretty similar. I like to be in exploration geology and I really like sedimentology. So yeah, just applied geo science.

    Erica:
    28:36
    Awesome. Cool. So can you describe for us the projects that you guys are working on this summer? Same project or different project?

    Sean:
    28:46
    TGS has kind of tasked us with, I'm putting together some potential prospects or ideas of places we can look and most of that's going to be happening, well, we think it'd be North America and North American basins. And so we've kind of gotten access to some of their pretty amazing software, access to a lot of different databases and kind of putting that all together for a big picture of something useful that they can hopefully use from our projects. So I don't know if you guys want to add anything.

    Geoff:
    29:15
    Yeah, I mean, for one thing with these projects that's been very helpful to leverage the software that TGS has, specifically Longbow and access to their wealth of onshore well data that they have there. So we've been kind of bringing all of that together to generate these areas where we think that we should move further into as a company.

    Hualing:
    29:40
    Yeah. Also the first two weeks we're like working separately. We each have a study area and it's just a information gathering and doing researches and moving forward. Right now we are working in pairs. So, me and Geoff, we are working on similar location and to do like a research in a more detailed way. Yeah.

    Erica:
    30:05
    So you guys mentioned the software programs you're using. So aside from Longbow, what other programs do you use?

    Cahill:
    30:14
    Um, a lot, a lot of work in Kingdom. But Longbow yeah. Longbow and Kingdom. I'd say probably the big two. Yeah. yeah.

    Sean:
    30:25
    Any, I mean, any time you talk about geology, Arc Gis is going to come up. So we've definitely been using that a lot as well.

    Erica:
    30:32
    Okay. And is that different than what you were familiar with, from school or is this the same training that you had?

    Sean:
    30:39
    Well, Longbow is completely different. You know, even looking at production data is not something that I, you know, geoscientists when we ever, we go through academia, we even get exposed to. We use Kingdom. But I think it's, it's more of on a limited basis. I've, I've really been able to work a lot with, the, the well interpretation suites here at TGS that I hadn't worked with before.

    Erica:
    31:03
    Cool. How do you, do you find that challenging or kind of a natural extension of what you are already working with?

    Sean:
    31:11
    I mean, I, yeah, challenging, interesting, different. The team here, the geoscience team here has been very helpful, with the different, features. I'd say there are bugs. Some people might say they're features with the Kingdom software. (Laughter) but I'd say challenging. Yeah, but, but in a good way, not, not as a, you know, wringing out your hands kind of way.

    Erica:
    31:33
    So what else have you guys learned besides Longbow?

    Geoff:
    31:37
    I think for me is just kind of seeing just like what a day-in and day-out sort of process is like. So like having worked in the field, I never walked, I've never worked in a corporate environment before, but just kind of seeing how teams integrate and work together, it's going to say I've never seen that portion before. And so for me it's been fun, you know, going from classroom and then getting the actual hands on application of what we learned in the classroom. That's what's been fun for me so far.

    Erica:
    32:01
    Anyone else agree? Agree, disagree?

    Sean:
    32:03
    I agree. Yeah. No, I mean another thing that I feel a lot of us, especially me and with my Phd projects, they're very wide scale. I'm not talking about basins, I'm talking about plates. And so it's been very rewarding to kind of zoom in. Even if we are still basin scale, that's a lot smaller than I'm used to. So I'm able to kind of get lost in the details more than I would in a very large scale study.

    Hualing:
    32:28
    I think also a good thing is we learn from each other. Like where were you working together? Yeah, we're getting familiar with the software and if any of us found something and others will get around and see what we found. And I think that's very important for us to learn.

    Erica:
    32:48
    Yeah, absolutely.

    Cahill:
    32:50
    Yeah, I think kind of going off that as well and we obviously us for come from different backgrounds in Geo Science and what we've worked in and we kinda bring those backgrounds and each of our own projects and we kind of can come together and help each other out in different areas that we might not be more experienced with, like certain, well log interpretations or mapping things, stuff like that. So, so yeah, it's, it is helpful to have a team.

    Geoff:
    33:14
    Good overlap.

    Erica:
    33:14
    What's it like working at TGS, culture wise? The people, the food?

    Sean:
    33:22
    (Laughter) well they treat us well here

    Geoff:
    33:24
    I was gonna say no complaints there. Yeah, I mean getting started in know there's always a learning curve, but I mean I guess as much of a learning curve as there could be, you know, everyone around here has been as helpful as possibly could be, you know, to help make that climb that much less steep, if that's a good way of wording it. But that's kind of what I would think.

    Cahill: 33:43
    The food is definitely good. Healthy. I like it.

    Sean:
    33:45
    Can't complain about free lunches.

    Cahill:
    33:47
    Yeah. But, but I mean I think the culture here is really, everyone's been extremely nice and even just within the geoscience team, a lot of nice guys; Cian and Alex, they've been so helpful with any questions we have, whether it be geology related or software related, and we've had company outings already. Going on Top Golf is super fun. Everyone's very open to meeting different branches and whatnot. So that was really fun.

    Erica:
    34:12
    Why did you apply? Did it, for TGS' internship program in particular?

    Sean:
    34:17
    Well. Yeah. So, our professor, me and Hualing, we have the same, advisor at the University of Houston. Dr. Paul Mann. And he was actually the one that reached out to us because, James, the head of the Geoscience Department here, had reached out to him looking for good candidates. and he had asked us if we wanted to, to join up. We, we kind of, you know, we researched it. We, I was, I talked to James on the phone and it just seemed like something, so different from what I was doing at the moment that I felt like it was a great opportunity to jump back. And it, I have absolutely no regrets.

    Erica:
    34:54
    Awesome.

    Geoff:
    34:54
    Yeah, my story is pretty much the same thing. My thesis advisor was, was good friends with James K and so he reached out to me and saying, pretty much the same deal as him. Looked into you guys, obviously cause say Jason, I met you before. So that, and also, the interns from last year, I was going to say I was good friends with them too. So I knew what they did. And so, here I am.

    Erica:
    35:17
    Any surprises along the way? Anything that you weren't expecting that you've encountered during your time here?

    Cahill:
    35:25
    I guess one thing is, it shouldn't be surprising, but I'd always is that I'm working with really big data sets. There's always lots of errors you have to put up with. And even with the amazing technology we have, there's always, there's always a human aspect to it, that's always interesting, that we've dealt with in our data at least so far.

    Hualing:
    35:44
    I think for me it's the flexible working time and my, yeah, he didn't request a specific time to be here or like a specific time to leave. So that's like really helpful for my schedule that I can make adjustment along and try to see by what time range works best for me. Yeah.

    Geoff:
    36:08
    Yeah, that's definitely been nice. I feel, like you said having to commute from Spring. I was going to say, getting to come in maybe later or earlier as need be. It's always definitely nice to dodge that traffic.

    Erica:
    36:22
    What are you guys looking forward to working on for the remainder of your internship here?

    Geoff:
    36:27
    Well, I'm really excited to see the end product of what we're doing, especially because, we're going to be presenting it to upper management, and presenting it to our, our geoscience team as well. I think that's really going to help bringing it all together. Cause right now we know we're all working on our separate areas as well. I mean, we're still two teams in a certain area, but it's still very much our own work. And so that, that finish line I think is going to be where it all comes together and I see more bigger, I see a bigger picture than maybe I'm seeing right now.

    Geoff:
    36:57
    Yeah. I think one aspect that I like about is, it's not just busy work. You know, we're actually adding value to the company with an end result. Kind of like what Sean said.

    Erica: 37:06
    No making coffee?

    All:
    37:08
    (Laughter) Danggit. For ourselves, we make coffee for ourselves.

    Erica:
    37:14
    Um, what advice would you give to other students wanting to intern here?

    Cahill:
    37:20
    Say like, don't be afraid to get into anything that you're not experienced with. Whether it's geology or software related. Since coming here, I feel like you can learn a lot from a lot of different people and there's a lot of different backgrounds here and people are all open to helping you or talking about their passion and their little branch of geology or geoscience. And so I would say don't be afraid to ask questions and go up to random people and say, hey, what do you do here? And what are you into? Because chances are they're happy or passionate about their job and you can probably learn something from it.

    Geoff:
    37:54
    Yeah. Maybe to add onto those, don't feel like you have to know everything beforehand coming in. Cause I mean you're not, no one's gonna know everything. Kind of like what Cahill said, there's plenty of resources around. You don't feel afraid to ask. No. Everyone out here is more than willing to give their time to help you out for what you might have a problem with. And we've had that reiterated to us time and time again. So, I mean, it's been nice to know.

    Sean:
    38:17
    Hmm. And, I don't know if before we talked about how we got the internship, and I feel personal connections are the biggest, you know, it's not about going on a website and clicking apply. It's about going to the conferences and meeting people from TGS and they're extremely friendly. We've all seen that firsthand. So I'd definitely recommend, and I, I would recommend it as well that you would get an internship with TGS, but just go up and see them during conferences, talk to them, ask them about opportunities, say, Hey, what are you guys doing? Be interested. and even if you don't get something out of it, that's fine. You're still gonna make connection, connections and learn about where the industry's heading.

    Hualing:
    38:53
    Yeah, I definitely agree with Sean, cause I met Alex on with, the person, our geoscience group, we met during the AAPG meeting at San Antonio and I talked to him and, he talked to me about his project and what I may be expecting for my interns. I think that definitely helped. And yeah, when I first day, when I came here, I saw him as, hey, yeah, that's, yeah. I feel like familiar and yeah, I'm more easy to get along. Yeah.

    Erica:
    39:28
    What have you gained during your time here at TGS that you're gonna take with you as you continue your studies and your career?

    Sean:
    39:36
    Everything we just talked about. Yeah, no, I mean that, that's a good sum up question. So the, the connections we've made with all the people here, not just in the Geo science team, every, every other team that there has that there is at this company. All the skills that we're learning with these different programs, the different perspectives we're getting because we're looking at, again, not just geological data, we're looking at, these problems more holistically. All that and above, I think is what we're going to take with us.

    Cahill:
    40:02
    Yeah. I think, you pretty much nailed it on the head. It's seeing the, the geoscience in an actual industry application in its own way. It's a lot of different moving parts coming together for an end product that's ultimately valuable and generates business. And then seeing how that works, you know, if on a fundamental level that's, that's pretty interesting and being able to be a part of, it's pretty cool. So.

    Erica:
    40:27
    Well, awesome. Well, thank you guys for being here. Thank you for talking with us today, and we'll let you get back to work.

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    EPISODE TRANSCRIPT

    Caroline:

    00:12

    Hello and welcome to Beneath the Subsurface a podcast that explores the intersection of geoscience and technology. This is Caroline Brignac from the well data products group at TGS. In This episode we'll explore our well data products and how they prove to be critical datasets for any exploration and development program. So go ahead and we'll get started with introductions for today's podcast. We've got Jason Kegel with us. Jason why don't you to tell us a little bit about yourself?

     

    Jason:

    00:39

    Sure. My name's Jason. I work with the geology group here at TGS. I'm a geologist I've been here for six years. I work pretty closely with our well data products and our seismic products.

     

    Caroline:

    00:50

    Awesome. Thanks Jason. We also have Ted Miranda with us. Ted, why don't you tell us a little bit about yourself?

     

    Ted:

    00:55

    Sure. Thank you. Caroline. Ted Mirenda. So I'm with TGS. Well, data products group. I've been here for 10 years now. A primary task was to bring production data to TGS and commercialize that product. It's been a lot of work and exciting.

     

    Caroline:

    01:12

    That's awesome. I'm really excited about having Katie with us. She's a production geologists for a super major. Katie, welcome. Why don't you tell us a little bit about yourself and your experience with TGS.

     

    Katie:

    01:21

    Thank you, Caroline. I am a recent graduate school graduate and I loved my time at TGS where I got to use Longbow and R360 and then I carried those things that I had learned and into my schoolwork in grad school and it's been awesome.

     

    Caroline:

    01:39

    So Katie, you started with us as an intern, correct?

     

    Katie:

    01:42

    Correct.

     

    Caroline:

    01:42

    That's awesome. Well, we're really excited to have you here and talk a little bit about what your experience with TGS, our products and how you use it in the industry. So one thing that we know a lot about TGS is that it's known as a seismic company. However, TGS offers a wide range of other products such as products in well data. Ted, would you mind telling us a little bit about the well data products division and how it's evolved over time?

     

    Ted:

    02:07

    Sure. I guess we can step back to 2002 when TGS officially acquired a little company called A2D that gave A2D's the resources to further go out and I believe in acquire Riley's electric log inventory. So that led to the largest commercial well log library. Other resources that TGS provided or enabled was the ability to digitize hard copies and raster logs to LAS. And that library has grown over time where I came into play now 10 years ago after growing the LAS library TGS made the the decision to what's next with well data, well, let's bring in production data. That's when I came into mix. We started building our production data library up. It's been a long challenging project, but it's really paid off. One of the things that critical decision we decided to do was not acquire any production data assets, but build that data from the ground floor up. That meant more work. But in the long run, it's a more valuable product.

     

    Caroline:

    03:25

    So when you talk about production data, what exactly are you talking about and what does that look like?

     

    Ted:

    03:30

    Well, we're talking about the full historical production record of every well in the United States. So when you think about different pieces of information that our clients use and need what the well has produced, the reservoir fluids captured from each wellbore is about as important a piece of information as you can have going forward. So we capture that information, really important to tie it to the proper wellbore and a really detailed well header record. There's a lots, a lot of other processes that we do with that as well to then provide the data to our clients.

     

    Caroline:

    04:16

    So we know that we have, Jason has some experience as well as Katie with this dataset. Would you mind telling us about how you guys use it in your role in the industry?

     

    Jason:

    04:25

    Sure. So I know at TGS we use the production data quite a bit, looking at our different mapping projects we have. So when we look across the entire, especially United States and look for new areas to shoot on shore seismic, we like to have a really good background information on what companies are actually producing, how much they produced in the past. Can a lot of times tell you where the, where the new plays are and it's always been said that where you found oil before you'll find oil again. And that's been proven over and over again. When we look at the Permian basin, which has been producing since, you know, the 1910, 1920s and today it's one of the biggest basins in the world and we're still finding oil there. So it's nice to really see those historical records of production and where people have gone. On top of that, the Longbow database gives you completion information so you can start really seeing where exactly within the geology has been drilled and how they have done it. So you can get some engineering insight into that as well. Over the years at TGS we've brought all that together to really start looking at new areas where clients want to go and where we can start bringing them seismic.

     

    Caroline:

    05:34

    So Katie, we knew that you started off as an intern here at TGS a few years ago and we know that you worked with Jason on his team to help sort of guide where we'd go next with our products. What was your experience with the production dataset and Longbow?

     

    Katie:

    05:48

    So I used the production and information during my project, both at school and at during internship to help me understand the reservoir better so that I could clear up any uncertainties that I was curious about. So for example, I use production data during my time at school to help me understand if there was any reserves left that were not taken out.

     

    Ted:

    06:19

    Yeah, I know a lot of our clients then use that data to look for bypass opportunities. Another one of the many capabilities of leveraging production data. Jason talked about moving into the completion data side of what we call completion data. Kind of led that evolution. You know, horizontal drilling, unconventional tight reservoirs, fracking, I mean that led to a whole new need for different attributes captured about a well record. So we identify those pretty early on. I had been collecting those and now provide that kind of information to our clients. Not just perf intervals. What is the, what is the producing interval subsurface depth, but the length of the lateral that's being completed and produced correlating production rates, any U R S 2000 foot laterals, another way to really do better well economics and evaluation of assets. So it's, it never ends, you know, the data needs are constantly evolving and changing as industry changes and we follow that path.

     

    Jason:

    07:38

    So Katie, you said that you use some of our production data with your thesis work, correct. And that was in the, in Louisiana, the Tuscaloosa Marine shale, right?

     

    Katie:

    07:47

    Yes.

     

    Jason:

    07:47

    So the Wells and the data that you used there, were they mostly conventional Wells or where we also tried to look at some of the unconventional Wells there too, to define that play that you are looking at.

     

    Katie:

    08:01

    Right. So I would say the majority, I also focused on the lower Tuscaloosa, which was mostly conventional Wells.

     

    Jason:

    08:09

    So those Wells, they helped you define that play area and then you had to go deeper and deeper into the log data. Correct. Trying to see exactly what the formation was made up. And you did a sort of a real exploration study of that lower Tuscaloosa Marine shale Longbow helped you kind of understand exactly where the production had become historically and where it might go now and where, where people are drilling currently in the Tuscaloosa Marine shale.

     

    Katie:

    08:39

    Right. And we also did that with the Austin chalk too. That was another one of our big projects.

     

    Jason:

    08:44

    Right. And then when you, in the group that was here all from the university of Lafayette worked with us, we also looked up into the Haynesville and looked at some of the smack over units using Longbow quite a bit, looking for trends in conventional plays historically and then seeing where those went unconventionally and if Longbow is the, the main generator of the majority of that data.

     

    Caroline:

    09:09

    So for those of you listening in who may not be familiar with Longbow is that is our our visualization tool that sits on top of our well performance database. Ted, would you like to add to that?

     

    Ted:

    09:19

    Yeah, that's right. So you know, production data is a fairly complex data model, right? So you need a tool to search and search your way through that data library, identify Wells that are appropriate to your project assignments so Longbow started out as really as that initial search engine. Hey, you're connecting to almost 5 million Wells, right? In a cloud based database and you're typically going in your assignment, you're going to identify subsets of Wells based on location, geology, formation, operator assets. Hey, examine these assets that are for sale and tell me if it's worth it, right? So Longbow provides that search engine. However, over the, the years and the time, we've incorporated quite a bit of analytics into the search engine. So we're really proud of that. It's if you can think of having a search engine connected to a live database of every well and include analytics, make a bubble in contour map on six month cumulative by zone, you know, all that in one. It saves time. So it's been exciting. We've had great feedback from clients and we are really focused on, Hey, what do our clients want? That's what we put in

     

    Jason:

    10:46

    When you go. When you talk about analytics Ted, what has been the biggest benefit of forecasting for Longbow?

     

    Ted:

    10:54

    Well, okay, so that is another good point. Production data being the historical production for the wellbore. Again, the reservoir fluid produced once me and my team, I felt we were comfortable and really good at acquiring that data. I always wanted to move into the forecasting realm as well. So we have added to the, to the product feature every single month. Now every, well all active wells get forecasted to their economic limit, giving our clients quick access to EURs. So from that perspective, I can look at historical data for an example like Katie gave about looking for bypass opportunities. Where did prior operators leave hydrocarbon in the ground with forecasting, I can look at, okay, what's the total proposed value of an asset? How much is that asset going to produce? How much remains that's already there in the, in the analytic tool. So, and again, the different analytic tools include besides mapping, probability graphs, scatter plotting and charts. It's the full gamut.

     

    Jason:

    12:08

    So we have, Katie who has worked with this data as an intern. I work with this data internally with project development and sales. And then I know that I've gone out with you before and we, we sell this data, we try to give our clients opportunities to use this data. Are our clients, strictly exploration type geologists or engineers or do we have other sort of venues where this data's important in the oil and gas industry?

     

    Ted:

    12:36

    You know, that's a good point. I mean, our clients cover all those gamuts. You know, one thing, again, with production data, it's a valuable piece of information across an integrated oil company. Enterprise exploration, geologists exploration is of course petroleum engineering department, reservoir engineers that have to forecast production. It's really become a big tool also in the A&D world investment banking A&D world at oil companies, business development. And that's what I like about production data. Everybody finds a use and value out of it,

     

    Jason:

    13:23

    Right? And it seems everybody wants to know how long that well is going to last and where the next well is next to it. It's going to produce as much that really hard to find that information from anything other than production data.

     

    Ted:

    13:33

    And what's, you know, what's, what's recently happened and I was looking at right, or like writing a paper on this topic. But you know, right now, most of the think tank forecast for supply, they're all like redoing those and lowering them, you know, the Unconventionals. And we, when we started doing our forecast models, we realized that the horizontal Wells had to be looked at differently. And the decline rates on those, those Wells now are, what would I say, exceeding what we thought they would be.

     

    Ted:

    14:08

    We had this, you know, unconventional production had made perhaps a real the world with the real comfortable setting of endless oil supply and and you see the think tanks now readjusting those forecasts. So our model changed as well. We're looking at studies and how long Unconventionals are really going to produce and readjusting the EURs. And does that also have quite a bit to do with parent child relationships and how they're stacking Wells within the reservoir? It does, and right now that's what everybody's trying to figure out. That is really challenging looking at spacing, refracking spacing, how does another child affect the, the, the parent well and etc. What is the proper spacing that we try and provide the data to our clients to help them do that?

     

    Jason:

    15:04

    Right. And in some of those cases you said before with our header products that we have, that really has led to Delineating some of the production data with the validated well header. Can you explain a little bit more about how the validated well header helps understand different laterals and how that traces back to production?

     

    Ted:

    15:25

    Yeah. Yeah. And that's that's another key point, I think what was attractive to building production data here at TGS? You know, you go out and collect production data and for the most part, I mean, when you're getting public production data, the reality is that data is really coming in at a surface level. I mean, what does the state regulator care about? They just want to know how much did operator produce. So your severance, you're paying severance tax below the surface, they're not so much concerned about which zone is that coming from in which borehole? So here at TGS we have, we can leverage our validated well header dataset, which is our proprietary header where we've gone in, looked at the subsurface and identified missing boreholes. So we are in the process of tying our production data now to that validated header. So really moving production data down to the, to the, what we call the 12 digit API level. And that's really making a difference to our clients.

     

    Jason:

    16:39

    I know it's helped internally where we've gone used the perforation information.

     

    Ted:

    16:43

    That's right. Yup.

     

    Jason:

    16:45

    And actually track the perforations. And I'm not sure if you, you might've done this with this, some in your internship, Katie, where we looked at the perforated intervals on the Wells and then when we are doing our cross sections, we would actually see exactly where the perforations were and see where that oil was coming from. And that helps in a lot of situations in basins where you, you don't know a lot about the basin or you're going somewhere new and you're mapping and we'd see, you know, you'd see the Austin chalk and the Buddha and the Eagleford and you try to wonder, well, where exactly in those formations are they getting the oil from? Without those perforations that we'd got from Longbow, we couldn't truly track that back. We've been doing that more and more with the help from interns when you were here a few years ago and also with our newer interns to, to really try to understand that and then provide that on another level through R360 to start understanding where these Wells are actually producing from, which in some states they don't, they don't provide that information.

     

    Ted:

    17:42

    That's right. And that that really is a really neat project. I know for me and my team at the, and Ted talking about the production data, leveraging Jason and the geoscientists and the interpretation type work you do on your workstations where we can take our production, our perfs, you guys load it in, match it up with the LAS, correlate that production to the actual producing zone. It takes a while to do that, but we're doing that in projects going across different basins and it's really exciting.

     

    Jason:

    18:15

    No, it's been, it's been very valuable for us that in some of the test information that Longbow has also has in some states like Oklahoma and Texas, let's say, they don't have produced water for a lot of the production. So the only things that you can look back are some of the actual, that the test data that you have where you can find that water. And then a lot of these areas where you're running analytics on some of these Wells to see when they watered out or how much water they have per volume of oil. That's the only place you can get it. And then when you max that match that back to the perforated interval, you can really start understanding some more about those horizons and how much oil or how much oil you have left, but also how much water you're getting out, which is a huge issue right now with a lot of the unconventionals is water not only how much water you're putting in to stimulate if that's what you're doing, but how much formation water you're actually taking out and that could be a, that could be that the factor in having a well that's a good well or not good at all.

     

    Caroline:

    19:19

    So I know we've touched on production data and the well performance database that TGS offers, but TGS also offers other data like well logs, various types of well logs our validated well header that Jason just mentioned. Katie, I'm curious about your experience as a student getting data from TGS. Can you tell us a little bit of what that was like and how you use other well data with production data to help solve some of the, the issues you guys were running into?

     

    Katie:

    19:48

    I'm sure. Well, TGS was really helpful because like Jason said, if Jason and Ted said to the state, you don't have to provide good data to the public. So TGS' well logs, their production data was far superior to anything that I saw. So it definitely helped not just at school cause I use this product at UL but I also got to use it in our projects. So it made the uncertainties that were, we were curious about less uncertain. Right. Cause the subsurface is always uncertain.

     

    Caroline:

    20:26

    So to follow to build on that, Jason, how do you, how do you work more with well logs and production data together, especially when you're working with a group of young interns like Katie and her, her fellow interns

     

    Jason:

    20:39

    Well one of the things that we do in our group quite a bit is either look for for new areas or sort of redefine basins that have already had had exploration. So the main thing we do when we do that as we get as many well logs as we possibly can. So that's the, the LAS that we have for those areas. Working for TGS is nice because we have access to quite a bit of data. So we pull all those together and we start just doing cross-sections and fence diagrams and make picking our formation tops so that we have a real good general understanding of the basin. As we're doing that, we're also looking at the production data. So each one of those Wells is either a producer or not a producer or maybe it was just a stratigraphic exploration well. But the reason those Wells exist are to make somebody money. So hopefully they're all producers.

     

    Jason:

    21:32

    So we learned as much from a dry hole as we do from a hole that's not dry. That's where the production data comes in really handy cause we can see exactly how much oil they got out of that well when it was drilled, when it was plugged and abandoned. Some of the issues that might've gone on with it. So we can understand from looking at just some of the well logs themselves than the caliper per se, to see where you had the whole breakup and see where you might've had engineering issues with that well, where they might have crossed faults that might've caused to loss of production in certain areas. And we can tie that back using production to see exactly how these reservoirs work. And we can track that around better to see where explorationists, might need help delineating new fields or new areas. And that's where the seismic comes in with TGS to where we can try to get the seismic out to help limit some of these problems that were we might be seeing in some of the Wells.

     

    Caroline:

    22:30

    Out of curiosity I know that we offer a long range of historic production data. Recently we just acquired a company called Lasser that goes back far beyond the 1970s. As a geologist, would you say that having a larger dataset going back further in time is more beneficial for you to help solve problems?

     

    Jason:

    22:54

    Absolutely. So the one thing we've always ran into is not enough data, right? We always want more data. We want to see the complete picture of the entire basin. So having that data that goes farther back in time, that historic production data really helps because we have a lot of those well logs that are sort of historic historics our well logs and our Las don't stop at 1970 or earlier. The production data depending on state isn't necessarily at a strict cutoff of 1970 but that historic data really helps with that production to really start understanding how those wells were drilled. And like I said before exactly what was it producer and what wasn't producer and if it was producing, how long did it produce for? There's been lots of of technology advances that have really increased how much oil you can get out of the ground or gas you can get of the ground.

     

    Jason:

    23:45

    That's on a purely engineering basis and you can start to see that in the production data, but you can really start seeing that in some of the LAS data when you start looking at the curves and understanding some of the petrophysics behind the Wells. And not only that, you start understanding the basin. So when you look at some of these really old wells, a lot of them are really shallow just to sort of understand that's as far as they could drill to. That's where the technological limit was. But depending on the basin, some, some people in the forties and 50s had drilled all the way to basement. You really want those type of data points when you're understanding the entire basin. The deeper you understand the basin, the more history you can put into it. The more basin modeling you can do. If you can understand the basin from initial infill to present day and the erosion intervals that have been between there. We see that quite a bit in our base in temperature models, which is one of the products that we do that builds off of our LAS data.

     

    Caroline:

    24:43

    What other tools, interpretation tools do you use internally that TGS helps provide or provides to our clients?

     

    Jason:

    24:49

    Well firstly I mentioned the basin temperature models. That's one that we, we helped build and we provide to clients and that's a product where we look at the entire basin. We pick the tops in it from 2000 to 3000 Wells from the LAS. And then we do basin temperature modeling on that entire basin with grids and horizons, start understanding the the basin from completed from basement all the way up to the top and understanding the infill. We also provide other products, sort of worldwide called our facies map browser. And this is mainly offshore, but this is looking at sequence stratigraphy within offshore basins.

     

    Jason:

    25:29

    This one we also use well data and seismic data where we can and integrate the two of them to, to have a real good understanding and picture of the basin. So the geologists that use this data can jump right in to the basin and have a real good working knowledge of what's going on there. One thing in the industry, I've been in this industry for eight years now and I've seen lots of mergers and you know, lots of layoffs unfortunately with people, but groups shrink and grow all the time. And when they grow, people need to jump into new basins they've never been. So one thing that we provide with some of our well data products like the facies map browser and the basin temperature models easily help people easily get acclimated with basins they may have never worked. It's a, it's a real quick and easy way to understand the stratigraphy and understand some, some components of the basin you might not have thought about before.

     

    Jason:

    26:25

    Then we've been moving on with the basin temperature model is that the background into TOC models. So actually looking at total organic carbon within the same basin using the background of our basin temperature model and then working with core labs to really understand some of our vitrinite reflectance and core data points. So that's the new thing we're doing particularly in the Permian basin.

     

    Ted:

    26:48

    And I want to add another point on Lasser that Lasser acquisition, which was a, again, exciting for our team. Jason talked about the need for historical data. Sure. acquiring that data set. Now, the only way you could really replicate that public data is if you went to physically went to the individual railroad commission, district offices and loaded up a bunch of microfiche. So that data's digital. We've got it now. What's really neat is we're running it through our modern QA and QC processes. So adding data production volumes in Texas all the way back to the 30s, and then taking further, taking the lease level production data and allocating it to a well level. Nobody in industry is doing that right now from nobody from a vendor perspective. So that project that's ongoing and will be completed before the end of the year. Having historical production back to the 30s allocated to the well level, excited about that and proud of our team to get that done.

     

    Caroline:

    27:55

    Not to ask a silly question, but what is microfiche is that what you said?

     

    Ted:

    28:01

    I said microfiche, yeah.

     

    Jason:

    28:01

    You don't remember Microfiche? (Laughter)

     

    Caroline:

    28:02

    You're talking to a millennial.

     

    Jason:

    28:04

    I feel so old.

     

    Ted:

    28:06

    The point there is the data is not digital, it's manual, it's on microfilm. Microfiche it's lots and lots of hours of labor to recapture that data in database format. And now that we've got it, it's going to be real exciting.

     

    Jason:

    28:27

    My experience with microfiche was always in elementary school going to the library. So at the library they always had stacks of microfiche that had historical newspapers from the past and you can still find them and they're really, they're almost like little slides like you remember, do you remember what slides looked like? (Laughter) No, it's done. That's true. It's already 2020. [inaudible] There was a special microfiche reader to see them. And you flip through each one of them. But that's how they always documented historical papers. So we'd go back and have to do research projects and you'd have to go find your little microfiche from the library. And when you looked it up, you would slide through and it was like a little projector screen that read the fiche from like the little, little tiny film and scrolled through the little film. So it is almost like a negative

     

    Ted:

    29:17

    It's a picture of a document. So I'm not the only millennial in the room. So Katie, I'm gonna make a safe assumption that you did not know what that was?

     

    Katie:

    29:23

    Nope, no, I had no idea what that was, but I have seen it in movies. So thank you for that visual like connected the two for sure.

     

    Ted:

    29:31

    That's right. But that, that tells you how you know how-

     

    Caroline:

    29:37

    How hard to find it, how hard to find that data is.

     

    Ted:

    29:39

    That's right. There weren't computerized records back then, but we still need the data

     

    Caroline:

    29:44

    Absolutely.

     

    Katie:

    29:44

    So you've talked a lot about onshore, so do you offer the same kinds of products offshore as well or what do you, how does it go from onshore to offshore?

     

    Jason:

    29:58

    That's a good question, actually, because with TGS and with the amount of data that we have onshore as really dense area of log data per se, so we can do areas like the Permian, the Eagleford or the DJ basin and fill them in with 5,000 Wells and pick tops and all 5,000 of those Wells. And they all have temperature points. So we can do our base in temperature models there. Offshore, it gets a little bit more difficult because there are, the data's not so close together and offshore particularly say in the Gulf of Mexico, the geology gets a little more tricky, particularly with basin temperature models because you start dealing with more salt. You start dealing with just having the water to sediment differences that you'll- we understand pretty well, but the more well data you have, the more we can make those interpretive products.

     

    Jason:

    30:55

    So we have, sort of, different products offshore and like I mentioned before, we have the facies map browser is almost exclusively offshore because we can do that along mainly 2D lines, so long 2D lines that go over large areas and are- usually have a few wells connected to them in exploration areas. So the newest one of those is what we're trying to start now in Mexico and the Mexican side of the Gulf of Mexico where a few years ago we shot a really large 185,000 kilometer 2D survey called Gigante. So we interpreted that whole survey and we shot gravity and magnetics over it. So we actually have a gravity and magnetics model that we've built on that area that helps a lot in exploration, but we've also interpreted all the seismic to pick certain horizons. We would like to go a few steps further and actually understand your stratigraphic facies and your sequence stratigraphy that's in there.

     

    Jason:

    31:56

    And that's what we're, we're trying to do now with the Mexican side of the Gulf of Mexico. And it's a little bit easier there because there's less wells there and a lot of the operators that are moving in there since they opened up Mexico aren't there. So they don't have as big a knowledge base as they do in the U S Gulf of Mexico. And that big large knowledge base in the U S Gulf of Mexico from the operators that have been there for 40 or 50 years has really limited multi-client type interpretation studies. Because say the Exxons or the Shells or the Chevrons have been in these basins for so long, especially the Gulf of Mexico that they have the working knowledge of those basins and they train their employees on that pretty easily. So they don't necessarily need an outside company like TGS to sort of give them the boost or the the heads up or the, the first step to get into a basin.

     

    Jason:

    32:53

    Whereas in other basins around the world where we have facies, map browsers, we've had them for a while, we have new companies coming in and going more often. So they sort of like having that extra layer of knowledge that we can offer on shore. In the Gulf of Mexico though we did do a post-well analysis, which is just looking at specific wells and I think we have a little over a hundred now and they're either dry holes or or discoveries and they sort of show the stratigraphy they show why it was a dry hole or why was it a discovery. We match that up with seismic and certain areas so you can see the structures that were being drilled at the time. So we do have that. And then in the Mexico side of Mexico and the Gulf of Mexico, we have production data on both sides now.

     

    Jason:

    33:41

    So we actually have the contract with the Mexican government to provide not only the seismic but the well log data in Mexico, but also the production data in Mexico. On the U.S. Gulf, we have the contract to deliver log data. So companies that drill in the U.S. Gulf of Mexico, they actually send their log data to TGS. We hold it for the 26 month timeframe. And then we clean that data up. We provide our LAS plus package. We provide that back to the BOEM or BSCE, the government entity that sort of controls the Gulf of Mexico. And then we also provide that to any other company that would like to purchase it. So we're the - TGS is actually, we've had that contract for a little over 10 years now and we've just renewed it this year.

     

    Katie:

    34:34

    So like how much coverage do you have in the Gulf of Mexico? Data-Wise.

     

    Jason:

    34:38

    Data-wise? So all of it really. So with the, with the recent acquisition of spectrum, we now have 2D coverage that extends all the way from Florida to the Rio Grande Valley really. So we have 2D coverage that covers, there are, TGS is a seismic company. Our core seismic area has always sort of been 3D seismic anyway, has always sort of been the Mississippi Canyon, DeSoto Canyon, Atwater Valley area. We have lots of 3D seismic. We're currently shooting seismic there. We'll just finished up some new nodal surveys there and doing reprocessing. But we have 2D and 3D coverage across the whole area and well data we have all of it. We have every well that's ever been drilled in the Gulf of Mexico.

     

    Ted:

    35:27

    On the production data song for Gulf of Mexico. The data's really, really nice from that perspective. I mean every well is reported oil, gas and water, monthly production. Well tests are extensive in the Gulf of Mexico. Perhaps the federal government does a better job of reporting well test data, making sure operators are testing those Wells annually and semiannually and getting that data out to public. So you also get access to certain pressure data in there, you know, flowing tubing pressure, bottomhole pressure, et cetera. So that data sets we like working with that. And now on the Mexico side, you know, we've got full coverage of Mexico petroleum industry. There's about 21,000 Wells with production in Mexico. About 1100 of those are offshore and we have captured and calculated monthly production for all of those Wells. So that was a fun project. Learning to translate certain wellheader attributes from Spanish to English that was fun to do. Converting units of measurement down there from a, you know, average daily rates to total monthly production. Bottom line is that data's now standardized in our library monthly oil and barrels in Mexico, monthly gas and MCF water in barrels. And,looking at the data, there are world-class wells in Mexico, so I think the continued release of data from Mexico. Hopefully we'll stay on track there with the, the government releasing data. Like I said, there's there's been some really gigantic flow rates down there, particularly offshore and no reason to think there's not great opportunity there. Seismic

     

    Katie:

    37:36

    Where's your seismic that you just shot in Mexico. Where does the location lies?

     

    Jason:

    37:40

    So the, the 2D seismic that's there, the original Gigante is all offshore and covers the entire Mexican Gulf of Mexico 2D. So it covers everything and it even goes sort of around the horn of the Yucatan near Belize. So it covers everything sort of almost into the Caribbean. We've also been doing looking at reprocessing efforts to extend some of our, to extend the seismic onshore to offshore and the Sureste and Tampico areas. And then we're also looking at 3D programs as well.

     

    Katie:

    38:15

    Very nice.

     

    Jason:

    38:16

    So there's quite a bit there. And that's not the only place that we have seismic or well log data. So TGS is actually always, I always try to remind me, we have well log data worldwide. So we have data. Do you know Russia and Africa and Australia and Malaysia all over Europe. And all over South America as well. And seismic too. I sort of focus on Western hemisphere so I know a little bit more about that part, but that's still quite a quite a large area sometime. And we're we're, we're looking at wells and seismic all across, both North and South American.

     

    Ted:

    38:53

    Don't forget Canada.

     

    Jason:

    38:55

    And Canada too, we have quite a bit of seismic in Canada as well.

     

    Caroline:

    39:00

    Nice. So one question I have for the table, we know that as TGS is predominantly a seismic company, but we also offer well data. How does that, how does that really help our clients when we offer two very different and unique datasets together?

     

    Jason:

    39:19

    I think the biggest part of that is making a complete geologic picture for explorationists. So you need the seismic to really sort of understand areas where we don't have well data and that well data really helps the seismic become better. One of the good examples of that is in some of our reprocessing efforts we're doing offshore, we're incorporating as much well data as we can, particularly Sonic data so that we can really understand the velocity models. And really make sure that we can tie those velocity models when they come out and with our seismic comes out in depth that our wells tie perfectly with them. The more well data we have, the better our seismic is going to be at the end of the day. We've always tied a few Wells that we can here and there, but since TGS has so much well data, it's a real benefit to our clients to be able to use that in the seismic processing and in reprocessing as more wells come out.

     

    Caroline:

    40:20

    So I'm just curious, you know, we are now offering a new product in the well data group. That's our analytics ready LAS that basically allows us to offer even more data. How do you feel about the machine learning algorithms that we're using in forecasting or with well logs? How do you feel about using that as geologists, Katie and Jason?

     

    Jason:

    40:42

    So one of the things that we've noticed quite a bit with this is you get a really nice big picture and particularly with analytics ready, we like to call it just ARLAS AR-LAS is that that big picture of that first presentation you can get, particularly when it comes to velocity models in Sonic where you don't have seismic. So one of the great images, and I don't know if I can explain this well through through radio, but one of the great images that you can have is with regular well data you have lots of lots of holes. So we didn't drill every place we could and then every place we drilled through time, we didn't do every log we could do. So a lot of the well logs that we have, particularly on onshore might have one or two curves. They might have a resistivity and a gamma ray or some of the older ones just might have an SP curve.

     

    Jason:

    41:32

    What can start doing with AR or the analytic ready Las is incorporate sort of Sonics into all of those logs and start understanding where we have those deviations in Sonic across the whole area where it hasn't been drilled. So from a big picture, it really helps you understand how that would tie together where you might want to drill next or what might, what interesting features you wouldn't see where a well isn't drilled without having seismic. And if you have seismic then you can tie them both together as well to kind of have a better understanding of of your depth processing.

     

    Ted:

    42:13

    And I might add onto that AI question back on the production forecasting a challenge. So we're offering both methodologies now of course we have our, you know, our traditional hyperbolic curve fit type forecasting algorithms that work well and offering the physics based you know, probabilistic spread forecasting new. Your question is how do we think about that? It's like, how does the industry think about that? I know everybody's talking about it. Everyone's trying to figure it out. To me, getting a million forecast in a couple of seconds is impressive. Right? And getting that full spread on each, well a P 10 through a P 99 forecast right at your fingertips. It's powerful stuff.

     

    Caroline:

    43:07

    Yeah. I'd be really curious to see where machine learning and artificial intelligence takes TGS in the future with other types of derivative products that we end up discovering and producing and really making sure that we're getting these to the industry to reduce cycle time. So I think that's pretty cool.

     

    Jason:

    43:22

    Yeah, absolutely. Yeah, I think we're, we're already moving in that direction with filling in log curves and in the seismic side trying to understand different seismic bodies. So using machine learning and AI to serve as a tool to understand where salt is in a quicker, more timely fashion or to even start understanding easier ways to define horizons or define some amplitude attributes as well.

     

    Jason:

    43:49

    [To Katie] So you've seen our data and played with our data and hopefully in the future is you're, you know, experiencing your geology career, you'll get to use it much more.

     

    Ted:

    44:01

    I think she's just scratched the surface with our data, right. I know all that data.

     

    Jason:

    44:06

    You had the unique opportunity to use it to come into our -come into the company and see what it was like to have that much data at your fingertips. Can you tell us a little bit about how, what that was like and how, how that's different from then to school to now that you're, you're in the industry.

     

    Katie:

    44:24

    So I came into TGS knowing nothing, well, not knowing nothing, but you know, minimal. You think you, every time you start somewhere you like think you know something, but you really don't, which I've learned again third time.

     

    Ted:

    44:37

    Right?

     

    Katie:

    44:37

    So at TGS, I wouldn't say it was just, I learned how to work with all this data, which was overwhelming at first. It was like I learned how to, I don't know, act, not just like socially in an office, right? But I also learned like what's important, what's not important. It's easy to get bogged down in the details when you go from zero to 100 real quick.

     

    Caroline:

    45:03

    So you've really had a unique perspective. Especially compared to a lot of us at TGS, you started off in an internship with us getting into the data and learning the data, applying the data. Right. And then I believe maybe you've even used it in your thesis.

     

    Katie:

    45:20

    Right.

     

    Caroline:

    45:20

    And now that you're in the industry, what has that looked like for you?

     

    Ted:

    45:26

    How about, how about how do you access data being an industry now?

     

    Katie:

    45:31

    When I've looked at data, it tells me, it makes me feel comfortable. It clears up uncertainties.. It's not telling me what's going on, but at least I'd like have more of a general idea. So when I look at these large amounts of data that I get for a project, let's say like I did in grad school, it's okay, I have this data. What does the data tell me? Does it tell me if it's pinching out? Does it tell me if it's, you know, this big chunk or maybe the depositional environment. That's what I looked at a lot in well logs the petrophysics.

     

    Jason:

    46:08

    No, it's understandable. You get thrown a lot of data in these situations and it's how you put that together, how you can efficiently use it. And that's something that we're always trying to make easier for people. It helps in a lot of situations, particularly in, in super major type of companies or in a lot of different companies, even smaller companies that they have geo techs that efficiently use our data before they give it to you. Right? So a lot of times you never, you'll never get to see the first part of, you know, where did this data come from because it all just ends up on your desktop. Right?

     

    Katie:

    46:42

    Right. So like I, what I liked about my experience I guess at TGS is I saw the beginnings, right? What a geotech would put it in. So I like got to see that visual fresh or put my own spin on it when we were using Longbow. So making those bubble plots or looking at URs and decline curves. I don't have, I don't, I haven't gotten that experience yet, but I'm a Guppy.

     

    Caroline:

    47:10

    So it was like you were getting access to data sets such as the, you know, the EURs and the forecasting database that you probably didn't necessarily have access to while you were working on your masters.

     

    Katie:

    47:21

    Right. And didn't know about until it came to TGS.

     

    Ted:

    47:26

    And the ability to build that project from scratch. I imagine a lot of times now in industry, you walk in and sit down and there are gigantic projects already existing and workflows established as opposed to like starting at the beginning.

     

    Katie:

    47:46

    Right. Which is overwhelming. Like I remember Jason was like, Hey, y'all are going to map from Mississippi, Louisiana and Texas. That was very overwhelming. Now I just, you know, you get a project and it, someone's already, most of the time, I don't know picked through it. So you don't, it's not very fresh.

     

    Jason:

    48:09

    But now you're not afraid of the deep end of the pool.

     

    Katie:

    48:10

    I don't know about that...

     

    Jason:

    48:10

    Right. We threw you right in the deep end and I, you can swim. You're ready to go.

     

    Katie:

    48:18

    Oh no. I'm still learning.

     

    Jason:

    48:18

    Well that's good. Never wanna stop learning.

     

    Ted:

    48:22

    We're all still learning.

     

    Katie:

    48:22

    Right. But I'm really still learning. As a new worker bee.

     

    Jason:

    48:30

    So Katie, is there anything we haven't seen you in a little while? I know that you're, you're in Louisiana now. Is there anything that you want to ask us that you're interested in from a, from your perspective after you've graduated and are now moving onto bigger and better things that might help you in the future?

     

    Katie:

    48:48

    Maybe not something that would- maybe wouldn't help me in the future, but also help other people that are looking for jobs. Is, are y'all looking for employment? Like looking to employ anyone or what does that look like? It sounds like you're doing a lot of work. So do you have people to fill these positions or are you, how does that go for y'all? Do you even know?

     

    Jason:

    49:10

    Well, that's one of those great HR questions where, you know, we're always, we're always just busy enough to need new people. (Laughter)

     

    Caroline:

    49:20

    And I think with, you know, new departments that were growing especially new datasets like Ted is talking about Mexico and Canada, I feel like it really helps to position us to grow, you know, as a company as a whole. So opportunities are always always coming up. Yeah.

     

    Jason:

    49:36

    I know particularly with our internship program, we're always looking for, you know, young, exciting new talent that can, that can come in and help us out. But also like you did learn about data from sort of the bottom up and take that knowledge base to other companies. So we don't only like training people to come and stay with us or we're perfectly happy bringing in interns and having them go out in the world and and learn something from us that they can bring somewhere else.

     

    Katie:

    50:06

    Oh sorry. I would say that that's why I like had not, I think that working at TGS was nice for others to see cause they knew that I had experience I guess with production data, which is a cool talking point I think.

     

    Caroline:

    50:22

    And just to build off of that, Ted has done a really great job building this new initiative, which is getting our well performance data in the universities to work with people like you, Katie, while you were getting your masters to make sure that we're able to provide data to other other programs and get geologists or young geologists access to data sets that they wouldn't have or wouldn't be familiar with whenever they're entering the workforce.

     

    Ted:

    50:48

    That's right. So, you know, we're happy to donate donate our products, donate production data and Longbow to the universities. As you know, at ULL they brought it into the geoscience and engineering groups. And now we're sitting on the, what the 20 workstations in the lab and part of the curriculum. So it's exciting at the same time, giving the students access to these data products learning actual, you know, working product tools. When they do get hired and hit the, hit the workforce, they're ahead of the game and ready to go. Now, from my selfish perspective, it helps to get feedback and make the products better. So it's a win win for both.

     

    Caroline:

    51:37

    Well, thanks everyone for coming out today and having this conversation, you know, hanging out, covering a lot of really awesome topics, kind of, you know, exploring where TGS is headed next, where we've been, where we're going. Katie, you know, especially thanks to you for coming all the way from New Orleans to sit with us and kind of give us your insight and your opinions and let us know how it's, how the journey has been for you. So thanks, Jason. Thanks Ted looking forward to the next, the next episode.

     

    Katie:

    52:01

    Thank you for having me.

     

    Jason:

    52:03

    Yeah, thanks Katie, it's been great

     

    Ted:

    52:04

    Thank you.

    Unlocking Latin America

    Unlocking Latin America

    In this episode of Beneath the Subsurface we're focusing on Latin America and how the recent Spectrum acquisition has enriched and expanded TGS' data library. Erica interviews Richie Miller and David Hajovsky, our experts in this prolific region. We'll explore the hottest regions in the South Atlantic margin as well as the bidding climate in Brazil and the path forward for data and technology investments.

     

    TABLE OF CONTENTS

    00:00 - Intro

    01:20 - Geopolitical Climate in Mexico, Argentina, Brazil

    07:12 - Frontier Activity in Latin America

    10:28 - G&G Technology Applications

    12:22 - Equatorial Margins

    15:02 - Investments in the Region

    16:47 - Brazil Bid & Licensing Rounds

    19:58 - Identifying Leads

    23:54 - Data, Beyond Seismic - Geological and Geochemical

    26:38 - Old Technology, New Applications, New Techniques

    30:00 - Predicting New Plays

    34:27 - Conclusion

     

    EXPLORE MORE FROM THE EPISODE

     

    EPISODE TRANSCRIPT

    Erica Conedera:

    00:00

    Hello and welcome to Beneath the Subsurface a podcast that explores the intersection of geo science and technology. From the software development department here at TGS, I'm your host, Erica Conedera. This episode we're focusing on Latin America and how the recent Spectrum acquisition has enriched and expanded TGS' data library. As you'll hear, Spectrum brings not only a strategic library of seismic data, but also a team of proven and qualified experts in Latin America. We'll explore the hottest regions in the South Atlantic margin as well as the bidding climate in Brazil and the path forward for data and technology investments. I'm really excited today to be in the studio with Richie Miller. He ran things in Latin America for Spectrum and David Hajovsky, our VP of Latin America. So we're here today to talk about how the spectrum acquisition is adding value to our library of data in the Latin America region. So to start off Latin-America is a huge region. There's plenty of geographic diversity there. What are some of the hallmarks of the industry in this region?

     

    David Hajovsky:

    01:20

    Yeah, well, I think first off, I guess, thanks for having us on here. It's a pleasure to sit here and kinda talk about something that I know Richie and I have both been working on for for a number of years now. I think for me, when I look at Latin America one of the big pieces is the kind of geopolitical ups and downs. You see where markets open markets close and it makes it complicating and interesting when it comes to trying to find the right way to invest there. I think a good example of that is Mexico. It's a market that had been closed off to foreign investment for over 70 years. And during the energy reform, it opened up and you had a lot of multi-client activity both from a spectrum and TGS. And now under the new administration you're seeing things take a turn in the other direction. So it's, it's interesting to kind of see how these things evolve and go and how it makes us manage and be very insightful about our business and how we make our decisions.

     

    Richie Miller:

    02:21

    And I think we're still real positive on Mexico. It's a huge footprint and the government's indicating they, over the next couple of years, they may move forward again. Like industry wants, it's a great opportunity there. And, we're, we're in a great position.

     

    David Hajovsky:

    02:37

    Yeah. And I think when you look in Mexico as an example on that, we're still seeing, despite some of the political rhetoric, when, when a more nationalist government gets in office, the exploration that's currently moving forward is still moving forward. You're still having seismic shot, you're still having wells get drilled. So that momentum is still carrying through. And, and that's the thing about our business. It's a long term business. So everything there, we typically ride out all political cycles. So it's just a matter of timing on how that happens.

     

    Richie Miller:

    03:07

    Yeah. And it's even longer for the, for our customers in the E&P world, they, they look at, at, at decades where we seem to be tied into a four to six, eight year cycle similar to Argentina. I mean, in Mexico, we have an election coming up in Argentina. But the talk to the, our customers, there's not a big concern. We may see a government flip there but it's longterm we're positioned for it. And I think it'll work out just fine.

     

    David Hajovsky:

    03:37

    Yeah, I think that brings up a, I mean we were both down in Buenos Aires for the ABG international conference. It's a conference of petroleum geologists and certainly I would think we both agree the, the views and the rhetoric coming from all the oil companies there who are our clients was very favorable, very positive on kind of longer term investment outlook. So this makes us feel optimistic about the region. And then just the business in general.

     

    Richie Miller:

    04:03

    Yeah, that's- and companies like Shell and Chevron, et cetera. They've been in country for quite a long time when there was a different government in place and different price controls. They're the same companies that came in and picked up blocks offshore. Not Chevron, but Shell was pretty aggressive. Yup.

     

    Erica Conedera:

    04:22

    What do you see happening with round two in Argentina?

     

    Richie Miller:

    04:25

    A round two is, is pretty exciting. So we have an election coming up. First elections actually late October. The way that's gonna work. If no one gets a majority, then there'll be a runoff in November. We understand from the government that they're going to announce this round the first week of November to open up in April and close in October. That works out real well for us because it hits this budget year cycle for our customers that are looking for some end of year deals. We've had the data that we'll be ready in February that's going to be in the Colorado basin that will be on that round. After the first round, we've seen more interest from, from industry that have come in and, and picked up some data. We even with the uncertainty in the election, we think round two is going to be a bigger, a bigger deal than round one, which was obviously a huge deal for the Argentinian government.

     

    David Hajovsky:

    05:22

    I think it's one of the things that it's an observation we have, that on that initial round. A lot of the players that end up participating are companies that have some sort of presence in Argentina already. You have a few new players that come in from the international space. But once you get that hub and you have some of that acreage, it makes the investment point that much lower. So as you move into around two companies that already have an established position are able to be more aggressive as they go forward. And because of the success of round one, we're also introducing more international applicants coming into to attract it. So it kind of builds up on itself, builds a scale that we need. And I think that kind of goes to a lot of the rationale behind the, the merger between Spectrum and TGS is prior to this, TGS would not have had the same type of conversations or the same position. But Spectrum has done a fantastic job of understanding the above ground environment and understanding the below ground potential and moving on that and allowing us to, to now work together and try to build a, a better position.

     

    Richie Miller:

    06:21

    What the ministry has indicated is they've put sectors out that cover the Colorado basin, the deep water area of a Southern area of Argentina as well as the ultra deep of the Northern and Southern parts of Argentina. So they've asked for the E&P business or industry to nominate specific areas. And a real positive thing for TGS is we've got that area completely covered with new data. It's really the only data that's out there to, to help with this round. It's just in this round. So they've also asked us to do some of our G&G work and, and nominate areas based on what we think is prospective. The good thing about our businesses is everybody has a different idea on prospectivity and that's why we see different companies bidding on different areas. And that works well for us.

     

    Erica Conedera:

    07:12

    So looking at other countries in the region, certainly Brazil has had a lot of activity, but what other countries do you guys have eyes on right now?

     

    David Hajovsky:

    07:18

    Well, I mean, a big piece of the market for, for both Brazil- I mean for TGS and Spectrum was Brazil, Argentina and Mexico. These are the big kind of established markets where you have a lot of investment already from our client's side and kind of justifies us being there in that way. We're always looking and screening all the other potentials that could be there. You can go down the list. I mean, what we're seeing right now, offshore Guyana where Exxon and now Tullow have had just a string of discoveries. It's really opening up new ideas and play concepts, not just for Guyana but along the entirety of the margin. And so I think that's, those are sayings that we watch out for in, from a business development point of view and try to understand how can that concept be an analog somewhere else that we're maybe not currently working or are currently working and trying to build up a new narrative to attract industry.

     

    Richie Miller:

    08:14

    Yeah, there's a, I think Apache's just spudding a well in Surinam, and it's right next door. So hopefully that will, will lead to more success for that that basin. There is a data footprint for the companies for TGS in Barbados and Trinidad. And we understand BHP moving forward with a potential well in Barbados. That's not been confirmed yet, but that's, that's positive at different play type. But there's always the thought that maybe the, the Cretaceous wonder basin underneath Guyana extends underneath Trinidad and Barbados. So there's a lot of activity and looking around in that, in that region right now it's pretty active.

     

    David Hajovsky:

    08:55

    And when you say, I mean, that's, that's to me been a key insight into the business and in my short time in the business is that new data opens up new ideas, new concepts. A lot of these places have had acquisition or seismic acquisition for 30, 40 years. And it's when you come in with new technologies and new ways of, of trying to acquire this that you can get different concepts and ideas that come out of that and that, that starts the whole new process of, okay, next round of exploration. Here we go.

     

    Richie Miller:

    09:26

    And that's really true for Trinidad, that there was a lot of MC activity in the 90s and early two thousands, and it's just been dead. Now there's they're L&G outputs going down there looking for new exploration. So there's opportunities and it could be reprocessing, et cetera. But you're starting to see more companies BHP, BP, Shell, all drilling new Wells to try to increase that gas production there. You know, gas is our future. So it's, it's Trinidad's working towards that.

     

    David Hajovsky:

    10:02

    Yeah. Especially areas like Trinidad where you have a a hundred plus years of production in place, you have a lot of legacy infrastructure. So the cost to get that to a economic point is much lower than being in a ranked frontier area for something like that. And it's for that reason that you do is, as Richie mentioned there, these companies will continue to invest in and explore there.

     

    Richie Miller:

    10:22

    Yeah. And Barbados is a great place to go visit for oil and gas. So

     

    David Hajovsky:

    10:26

    Yeah, I can imagine. Well, if got your Barbadian shirt on.

     

    Erica Conedera:

    10:28

    So you had mentioned, using other G&G technologies in the region. Can you talk a little bit more about that? What exactly we're using? What's exciting to you?

     

    David Hajovsky:

    10:39

    Yeah, so a lot of we tried to think about, and we, we interact a lot with our clients, try and understand what are the tools that they need or what are the types of data they need in order to de-risk these positions and decisions. And, you know, historically 2D seismic is your, is your frontier tool. You go in, can acquire regional grid at a relatively economic basis. It allows for large screening and then you'd move on to 3D seismic to go beyond that. But I think TGS, in recent years we've taken an approach of looking and introducing different technologies. So for example, we've been working with multi-beam and coring data to try to build a larger geochemical database. So we have the geophysical database and now we're building up the geochemical database and you integrate that data in and you're able to update your geologic model. And these are the sorts of tools that, that explorers who are our clients can then utilize to better de-risk their position in decisions.

     

    Richie Miller:

    11:37

    Yeah. One of the I think both companies (TGS & Spectrum) or one company now, that we is, how do we generate derivative products to generate additional revenues off of these, you know, some of the legacy surveys. And I know that a, we were working on some different potential fields, products in Latin America. It's still trying to get traction with, with these, E&Ps or some exploration products. The, you know, then you add in the multibeam products and things like that. It's really what do the customers need and what will they pay for. And, and we're starting to get, go down that path to find out what's gonna work and what won't work.

     

    Erica Conedera:

    12:22

    So David, you had mentioned Guyana and activity in Brazil. Can we go back to that a little bit?

     

    David Hajovsky:

    12:27

    Yeah, I think in part of what we see when, when all companies are having the type of success they're having in Guyana and testing play concepts successfully in testing new concepts, we didn't think about where those analogs might be. And I think one of the areas that we think has a lot of untapped potential is equatorial margin Brazil. So we were just going further down the coastline really. And, and one of the issues we have is you've had some very successful license rounds up there. You've had some seismic shot and certainly one of our plans is to continue to invest on a geophysical data because we feel it's needed, but we need to see some drilling activity. And that's been one of the slowdowns in the ability for the Brazil equatorial margin and truly get unlocked is from a permitting point of view, regulatory point of view. It's been very slow process to get Wells permitted and then drilled.

     

    Richie Miller:

    13:21

    Yeah. That too. To move to the next phase we need wells drilled in an equatorial margins. We've been working with the government on that. The government knows that the oil and gas companies and our customers are working towards that. We understand that a, there's, there's two, two big permits that the industry is watching. It's a BP permit and a Total permit. And, in the Amazonous region, we understand those permits are very close. We anticipate seeing a well drilled there next year sometime. Well let's hope that moves forward. Those leases were granted in 2013 so they should be onto the second phase of their exploration period, which then they ended up dropping some of that acreage, which spurs our activity in sales in the, in the data there we own that area of, of Brazil from French Guyana around the corner to Potiguar.

     

    Richie Miller:

    14:15

    And I think we've only seen seen two or three Wells drilled since that round. And there's been a couple of rounds since then. There was 14 or 15 with some scattering of acreage. But to really take advantage, Brazil needs to get these Wells drilled and, and they know it. They, there's a very large push within the government. You know, it's a relatively new government administration and, and they have license rounds that are scheduled out through 2021. We'll see a lot of acreage taken. But again, I go back to, we have to have Wells drilled and that's what part of our, our whole strategy in Brazil with the, with the team we have working there is to work on the political side as well.

     

    Erica Conedera:

    15:02

    So from what I'm hearing, you're not seeing a lot of investment in the region. How does that impact your own investment in the area?

     

    Richie Miller:

    15:10

    Well, there actually is a, some investment from TGS coming up in the equatorial margins. The, the pioneer, which is a, a BGP vessel that's worked for us for quite some time. It will be mobilizing into the Para-Manhao area of Brazil in early November. And we're going to acquire about 10,000 kilometers. It's an infill program of one of the Fugro surveys we've picked up. We're starting to see movement in our in our client base on, in that area. And it's a sector and round 17 is right in the middle of it. So we'll, we'll acquire this survey. We'll have it processed to be available in probably April of next year. So it is a continued investment. It's also an area that, that we see some lookalikes to the Guyana plays the Ranger and, and also Liza discoveries. It's pretty exciting that that Brazil can can have instead of the salt basins that, that is very prolific as we, we, we see the opportunity for a whole new oil and gas province to open up. What about a consultant named Pedro Zalan it's been doing quite a bit of work up there and he's he's working on a new area there right now that we will be presenting at an exploration seminar that, that we have scheduled for November 7th here for our new venture customers. So we'll during that seminar we'll be showcasing really an Atlantic Margin portfolio of projects and and he'll be speaking at that.

     

    Erica Conedera:

    16:47

    So you guys mentioned bid rounds in Brazil. Can you explain how these bid rounds work for those of us who are not in the know such as myself?

     

    David Hajovsky:

    16:54

    Yeah. So so Brazil's a, an interesting place. They actually have a number of different types of, of contracts that they offer up in these bid rounds. So they have what they call concession licensed rounds. So these are areas that are outside of the, the salt basins. Back in 2010, Brazil, after having some of these massive pre-salt discoveries, the government made a decision to kind of hive off an area that they call the pre-salt polygon. And within that area, a new acreage opportunities were kind of pushed to the side and for the time being, and outside of that is where you could get acreage if you're an outside investor starting in 20, well, they've, they've gone through a multitude of different things. But starting in 2017 there had been a hiatus on rounds and Brazil brought them back in a big way. So the concession license round means an oil company enters into a concession contract where you just pay a royalty fee. Inside of the pre-salt polygon, they offer up what they're called production sharing agreements. And so what companies are actually bidding on is profit oil that they would pay to the government. So as they move into production, they agree to pay X percentage to the government as a result. So it's just different mechanisms by which the government is able to recoup some of their, their resource or, or monetizing their resource. I should, I should say.

     

    David Hajovsky:

    18:16

    And Brazil is also introduced to a new thing called the open door policy. So open round and effectively like a lot of open door policies, companies can come in, review the data and we have some of this data that we're reworking right now to try and promote that. But then they would put an offer on a block on a given set of minimum. And then if nobody counter bids and they're able to take that acreage. And what this does by having these very different round mechanisms out there, you have a multitude of, of companies and players that come there. So for the pre-salt rounds, which are the production sharing contracts, you tend to have a very large IOC. So the international oil companies some of the larger national oil companies because these are very capital intensive investments. You need to have a big balance sheet and a big portfolio enabled to do that. On the concession rounds you'll see the same mix of players, but you also introduce some of the more independent companies, so a little bit smaller and more exploration focused and they're able to get some of the, the acreage that's away from the salt basins.

     

    David Hajovsky:

    19:17

    So a little bit lower value point in terms of getting acreage access and if they're able to work that up and do it in a way that is accretive to their portfolio. And with the open door policy, I think Brazil is really trying to push to even another tier of players to bring smaller companies, both local Brazilian companies and international companies to help diversify the mix of, of players that you have in the place. And so for a company like ourselves, we try to provide data that's going to target all, all three of these. And having a larger client mix is always a good thing. It allows us to take more risk and allows us to feel comfortable with taking that risk because there's more need for the data products that we create.

     

    Richie Miller:

    19:58

    It's encouraging that we're seeing a new entrance into Brazil. And just recently within the last quarter, we've seen, two new companies come into license data that, that are currently not players in Brazil that is very positive compared to some other other areas in the world. But they're looking for these smaller opportunities, like David said, on the, these permanent round blocks. And we have every permanent round block is covered by some sort of TGS data, legacy data, some of the new data that, that we've acquired. And extremely positive. They, they're coming to us. There's nowhere else to go to right now. We're working in this data where we have a G&G group in the Houston office here and also over in Woking that that help with identifying leads on this data that help us push out to clients. So traditional way of just selling the data in a line by line basis based on the line quality, the data quality, we're taking that a step forward and, and developing leads by a group of explorationists. These are people that have worked with oil and gas companies understand what oil and gas companies are looking for. And that's what we're being, we're, that's what we're pushing out to market right now.

     

    David Hajovsky:

    21:14

    And I think one of the interesting things that we see on that front historically for these sort of G & G value add products the, the client mix for that are tend to not be the super major clients. They have their own internal staff that will work and do that. It's kind of into their, their value point. But it's typically made for companies, smaller companies that may not have the resource for that sort of staffing or certainly some of the national oil companies who like to have different viewpoints and perspectives. But I think what we see now in today's world, even the super major clients see value in what we're providing there. And I think a lot of that is kind of based on the quality of, of the, the staff we have and the work effort that's being put there. So it's a, it's certainly helped us to better understand what our client needs are and the way they're kind of thinking about problems and allowing us to better address those problems in a way.

     

    Richie Miller:

    22:06

    Yeah. And I, I think it's, it's also on the investment side. We're, we're saying new ideas based on, on the data that's been interpreted that helps us develop more programs and, and make those investments that we have planned over the next few years in Brazil. And Brazil is open for business and we're going to hit it in a, in a big way. We speaking to the rounds on round 17 we've just completed a, a Potiguar 3D survey. It's about 10,000 kilometers and there's, in round 17, which will be next year. There's about 4,000 kilometers of that. That's over open acreage that we're seeing companies that are, they're interested in that. It's, it's gonna provide that, that opportunity for the industry really, and we're not seeing that much in Brazil, but they're going to have 3D prior to the rounds. Versus the, just the 2D portfolio.

     

    David Hajovsky:

    22:59

    Yeah, I think that was one of the things that that we've tried to do is, I mean, as a, as a geophysical contractor, we want to make sure we can provide the best quality data ahead of a round and for this upcoming round 16, which is less than two weeks away at this stage we were able to get out there and get 3D data ahead of the round for both Campos Basin and the Santos Basin. And these are proven to be very well received by industry. These are the type of products that helps them de-risk major decisions, I mean, when we talk about Campos basin, one of the blocks that's on offer there, the minimum signature bonus. So this is what an oil company is going to be obligated to pay at a bare minimum is $350 million for one block. And so to have the seismic that's going to de-risk that structure and allow them to better understand what the real potential is there, it's a, it's a huge benefit. So we're, we're happy to be able to provide that.

     

    Erica Conedera:

    23:54

    So you had talked about the different G&G data products that we're offering aside from the 2D and 3D seismic, can you talk a little bit about what else?

     

    David Hajovsky:

    24:05

    Yeah, so we, we've been offering we've touched on some of the derivatives that you receive off of the 2D and 3D. So work effort that happens beyond that, can be something as simple as an interpretation, can be different kind of attribute work, different sort of packages that we can customize for whatever the client needs are, integrating different data types. So TGS, I mean, obviously the, the Wells business is a huge piece for us. So this is where we would go into a given country, get access to their well database. A lot of times this data is very old. It's very spotty. It needs a lot of cleanup. So we've kind of honed that process down where we're able to take these well logs cleaned them up, make them interpretable, integrate them into packages that our clients are able to access.

     

    David Hajovsky:

    24:56

    On the geochemical side, we've been doing a lot of work effort with these large scale multi-beam projects. As an example in Mexico, when that market opened up, we acquired a multi-beam over 600,000 square kilometers of offshore Mexico. So effectively covering everything and utilizing that data, we're then able to high grade a coring location. So piston course something that oil companies have done from for a very long time, for 50 plus years. But by using this technology of the high res multibeam data, we're able to better high-grade where to take these cores. They'll find the right sort of areas to, to try to find hydrocarbon samples on the sea floor. And, and what we found is a very high success rate there. And you're able to correlate that back. And so for oil companies, when they're trying to do their, their basin modeling and understand where they need to be thinking about these are the types of data sets they can integrate in with our regional seismic or 3D seismic and better de-risk the play.

     

    Richie Miller:

    25:55

    No, it's, it's what, what do our customers need and that that was one of the items in a multibeam that came back and, and it's, it's working with our core key customers to understand what they need and what else we can provide. And the industry is changing that way and it's real positively. You put the two companies together, there's a lot of opportunity and a lot of geographic space to, to put together products.

     

    Erica Conedera:

    26:22

    In our last episode, we actually talked about multi beam, so we had a whole episode on that.

     

    David Hajovsky:

    26:26

    Good. Well then they've dove, they know a lot more about it than than I do, which is which is a good thing cause then they can go focus on that.

     

    Richie Miller:

    26:34

    Yeah. When I listened to it, I learned a lot more about multibeam.

     

    David Hajovsky:

    26:38

    No, but I think it's a, one of the things is it's taking old technologies and applying them in a new way. It's just like reprocessing data, which is a big part of our, our businesses. When you have legacy data, so data that might've been acquired in the 90s in the 1980s even more recent vintages, a lot of times the, the processing flow and the algorithms that were used to try to create an image were, were very antiquated either by a limitation on compute or for just the limitation in the code. But even taking legacy data and applying today's technology on it, we're able to see significant uplift. And, and a lot of times we'll go and capture that data and try to uplift that data to help compliment in what we're doing from a new data acquisition point of view. And it helps us better set the parameters on this new acquisition to ensure that geophysically, we're going to address the geologic problems in that area.

     

    Richie Miller:

    27:32

    Yeah. Imaging technology is, is we try to keep up with it on with the acquisition is not changing a lot, but imaging technology changes day to day. It's it's really breakthrough technology that's coming through and helping the E&Ps discover more resources and, and it's a big part of TGS is moving that imaging into the next the next phase.

     

    David Hajovsky:

    28:00

    And I think we've seen in, you know, you can take data sets that were acquired five years ago and, and processed with the latest and greatest five years ago and applying the technologies today. And we'll talk specifically about technologies like full wave form inversion to help better resolve the velocity field and you'll see a significant upgrade in that image quality. It's probably tantamount to the photo quality I have on my iPhone 11 compared to on my original, you know, iPhone three. If they even had that name back then, I mean, it's it's incredible resolution and detail and it's those sort of upgrades and insights that allow people to think about different plays and different concepts in ways that we need to be moving the needle.

     

    Richie Miller:

    28:48

    It's a big part of our business is we have to have refresh data ready when the opportunity arises, whether it's a discovery well there's a discovery that spurs is a, is a good tar trigger on, on, on sales of data. And then for license round, sometimes they surprise us some of these governments. And if we don't have that, the data ready and it's been reprocessed with the latest technology we may miss. So it's our job to identify what we think will, will the be, the surveys that we need to upgrade. Yeah.

     

    David Hajovsky:

    29:22

    Yeah. I think it's a, when you, when you mentioned that thing about the licensed rounds gets sprung upon us, it's Brazil for this round 16 that's upcoming here in two weeks time. When we were talking about trying to get 3D data ahead of the round that was certainly one of the big challenges we had was how can we, under this limited time frame and the way that this round has been earmarked, how can we get out there with a vessel, acquire the data, process the data, get a workable product to the client base. And it puts a lot of pressure on us to come up with creative solutions. But I think in most of these instances we've been able to luckily enough, stay ahead.

     

    Erica Conedera:

    30:00

    So it sounds like one of the challenges is predicting where the next big play is going to be. What about the Santos Campos?

     

    Richie Miller:

    30:08

    Well, I think, you know, that's a great question because back geez, it's been two years ago now, we, we made, we took the risk to move offshore into the outside the BEZ or the Brazilian economic zone which was out at that point. It still is at 200 miles. We started acquiring a survey and with TGS we, we partnered saw the opportunity and it's a new play, very similar to what's inbound on, on Santos. But some of the, some of the data we're seeing already and some of the experts that are working that they think it can be just as big as what's already been discovered in the Santos Basin. And so we're, we're talking 30 to 50 billion barrels. It's a big number to, to even throw out there because people will disagree with you. But we've, we've made an investment already. We've acquired 7,000 kilometers, 8,000 kilometers. We're going to go ahead and pick up the rest of that later this year. It's a big risk. But I think there's a very big reward for TGS and, and also our customers cause we're going to provide that data 3D data instead of 2D data before the rounds. And we're hearing that, that, EEZ (Exclusive Economic Zone). The rounds good chance there'll be offered in and round 18, which will be in 2021, which gives us a good time to plenty of time to get the data processed and out and for the customers to interpret it and have it ready for the round.

     

    David Hajovsky:

    31:49

    Yeah. And I, and I think that, you know, it, it is true. It is risk, but I think it's calculated risk. I mean, when just talking about the, the UNCLOS (United Nations Convention on the Law of the Sea) process. So this is the process by which a company can extend out their current exclusive economic zone. Brazil was the second country to apply for that back in 2004. So these things take time. But certainly I think what we saw as we looked at that area is there's great momentum. The government realizes there's good resource potential there. Technically it makes sense to extend this out. And you're getting all the right stakeholders in place, both with the UN with the Navy, with A and P with the government to, to see this move forward. And so, yes, it was a risk. It was a calculated risk. But I think it's the, certainly gonna prove to be the right decision where I've seen that I think kind of payout in itself

     

    Richie Miller:

    32:41

    That, yeah, that's right. That we and Argentina, they've, they've been granted the rights and Uruguay has been granted the rights. There's a few little areas in Brazil they're still working on in New York. You know, ironically Pedro Zalan on who we we mentioned earlier is working with the UN and the Brazilian government on that. Our country manager draw credit has been very involved in this whole process with the couple of the universities. We're, we're the only ones that have data that, that show the prospectivity outside the 200 miles. And we're using that and, helping the government move forward and we expect some very big results not only out of the expiration but also out of for TGS on the, on the data sales.

     

    David Hajovsky:

    33:26

    Yeah. And I think that this is the, this is part of the positioning, right? Is that we want to be viewed as allies to the governments and we're trying to help them promote their areas as we're trying to help our clients promote their own interests. And so it becomes a mutually beneficial relationship among all three. And so this has been the key strategy for, for TGS and Spectrum, and now we're bringing those strengths together.

     

    Richie Miller:

    33:52

    Yeah. Yeah. We, it's a footprint that we're putting together that with, when all said and done, we'll probably end up with about 40 to 50,000 square kilometers that's continuous. It's it's a must be basin. We have to be in Santos and Campos similar to some of the large basins and, and in the U S a on shore with the sale markets. They're the hottest basins in the world right now. And TGS is in, in all of them.

     

    David Hajovsky:

    34:20

    Yeah. We hope to continue that and I don't see any reason why we won't be able to keep moving that ball forward.

     

    Erica Conedera:

    34:27

    Well guys, it sounds like a, you have a lot of work ahead of you and we're definitely very excited about the value that the spectrum acquisition has added to our data libraries. So very glad you guys could be with us today.

     

    Richie Miller:

    34:39

    I appreciate it. It was it's going to be a fun group to work with. The the, there was a lot of success with this library, you know, not only in Latin America but in Africa and other areas of the world that, that we've added to. But it was, it's a top down approach that you know, the support, getting the financing to do some of these projects, the processing groups the finance groups, you got to invoice this. Everybody's touching it. Everybody in the office, the, you've got the, the it groups and the computer centers. It's, everybody's working on this together and it made it successful. So it's it's now to capitalize on the opportunities moving forward.

     

    David Hajovsky:

    35:18

    Yeah, and I think that it's a, it's a huge benefit to TGS to be bringing in this, this established Spectrum team. I mean, these guys have proven track record and we're creating, I think one of the strongest teams in industry. I think we could be Dallas Cowboys-like probably mid nineties Cowboys on that Superbowl run, I think is probably where we'll end up being. We'll see what happens this year.

     

    Erica Conedera:

    35:43

    All right. Thanks guys.

    A History of Seep Science and Multibeam for Exploration Today

    A History of Seep Science and Multibeam for Exploration Today

    In this episode of Beneath the Subsurface we turn back time with Daniel Orange, our ONE Partner for multibeam technology and seafloor mapping - and incredible storyteller - and Duncan Bate, our Director of Project Development in the Gulf of Mexico and Geosciences. Dan takes Duncan and Erica on an expansive journey through time to meet a special variety of archea that dwell in the impossible oases surrounding sea bottom vents. We also explore the relatively recent discoveries in geoscience leading to seafloor mapping and how seep hunting offshore can enrich the exploration process today.

     

    TABLE OF CONTENTS
    00:00 - Intro
    03:35 - What is a seep?
    09:06 - The impossible oasis
    11:45 - Chemotrophic life
    24:15 - Finding seeps
    26:51 - The invention of multibeam technology
    30:11 - Seep hunting with multibeam
    32:48 - Seismic vs. multibeam
    34:43 - Acquiring multibeam surveys
    44:32 - The importance of navigation
    46:20 - Water column anomalies
    49:12 - Seeps sampling and exploration
    56:23 - Multibeam targets
    59:12 - Multibeam strategy
    1:03:11 - Reservoir content
    1:06:44 - A piece of the puzzle
    1:10:21 - Conclusion

    EXPLORE MORE FROM THE EPISODE

    EPISODE TRANSCRIPT
    Erica Conedera:
    00:00:12
    Hello and welcome to Beneath the Subsurface a podcast that explores the intersection of Geoscience and technology. From the Software Development Department here at TGS. I'm your host, Erica Conedera. For our fourth episode, we'll welcome a very special guest speaker who offers a uniquely broad perspective on the topic of sea floor mapping. We'll learn about the technology of multibeam surveys, why underwater oil seeps are the basis of life as we know it and how the answer to the age old question of which came first, the chicken or the egg is the Sun. I'm here today with Duncan Bate, our director of projects for the US and Gulf of Mexico. Do you want to go ahead and introduce yourself Duncan?

    Duncan Bate:
    00:00:56
    Sure, yeah, thanks. I basically look after the development of all new projects for TGS in the, in the Gulf of Mexico. I'm here today because a few years ago we worked on a multi beam seep hunting project in the Gulf of Mexico. So I can share some of my experiences and - having worked on that project.

    Erica:
    00:01:15
    Awesome. And then we have our special guest star, Dan Orange. He is a geologist and geophysicist with Oro Negro exploration. Hi Dan.

    Dan Orange:
    00:01:24
    Good morning.

    Erica:
    00:01:25
    Would you like to introduce yourself briefly for us?

    Dan:
    00:01:28
    Sure. Let's see, I grew up in New England, Texas, so I went to junior high school, just a few miles from where we're recording this. But I did go to MIT where I got my bachelor's and master's degree in geology, then went out to UC Santa Cruz to do my PhD and my PhD had field work both onshore and offshore and involved seeps. So we'll come back to that. And also theoretical work as well. I had a short gig at Stanford and taught at Cal State Monterey Bay and spent five years at the Monterey Bay Aquarium Research Institute. Again, pursuing seeps. I left MBARI and started working with the oil patch in 1997 and it was early days in the oil industry pushing off the shelf and heading toward deep water and seeps were both a bug and a feature. So we started applying seep science to the oil industry and have been doing that for oh, now 21-22 years.

    Dan:
    00:02:32
    The entire time that I was at Embargin, and working with the oil patch. And in fact, ongoing, I do research for the US Navy through the Office of Naval Research. It started out involving seeps and canyon formation and it's evolved into multibeam seafloor mapping and acoustics. And that continues. So in the oil patch I was with AOA geophysics, we formed a company AGO to commercialize controlled source EM sold that to Schlumberger. And then we formed an oil company, Black Gold Energy, that would use seeps as a way to, go into oil exploration. And we sold that to NYKO, since leaving Black Gold with Oro Negro. We've been teaming with TGS since 2014 so now going on five years mapping the sea floor, I think we just passed one and a quarter million square kilometers, mapping with TGS as we mapped the sea floor and sample seeps, pretty much around the world for exploration.

    Erica:
    00:03:35
    Awesome. So let's begin our discussion today with what is a seep, if you can elucidate that for us.

    Dan:
    00:03:41
    So a seep is just what it sounds like. It's, it's a place on the earth's surface where something leaks out from beneath. And in our case it's oil and gas. Now seeps have been around since the dawn of humanity. The seeps are referenced in the Bible and in multiple locations seeps were used by the ancient Phoenicians to do repairs on ships they use as medicines and such. And in oil exploration seeps have been used to figure out where to look for oil since the beginning of the oil age. In fact that, you know, there seeps in, in Pennsylvania near Titusville where colonel Drake drilled his first well, where Exxon, had a group of, of people that they call the rover boys that went around the world after World War II looking for places on the Earth's surface that had big structures and oil seeps.

    Dan:
    00:04:39
    Because when you have a seep at the sea floor with or on the Earth's surface with oil and gas, you know that you had organic matter that's been cooked the right amount and it's formed hydrocarbons and it's migrating and all those things are important to findings, you know, economic quantities of oil and gas. So seeps have been used on land since the beginning of oil and gas exploration. But it wasn't until the 1990s that seeps began to affect how we explore offshore. So that's seeps go back to since the dawn of humanity, they were used in oil exploration from the earliest days, the 1870's and 80's onward. But they've been used offshore now since the mid 1990s. So that's, that's kind of, that seeps in context.

    Duncan:
    00:05:31
    But it's actually the, I, the way I like to think about it, it's the bit missing from the, "What is Geology 101" that every, everyone in the oil and gas industry has to know. They always show a source rock and a migration to a trap and a seal. But that actually misses part of the story. Almost every basin in the world has leakage from that trap, either, either directly from the source rock or from the trap. It either fills to the spill point or it just misses the trap. Those hydrocarbons typically make their way to the surface at some point-

    Dan:
    00:06:04
    at some point and somewhere. The trick is finding them.

    Duncan:
    00:06:08
    Yeah, that's the seep. And thus what we're interested in finding.

    Erica:
    00:06:12
    As Jed Clampett from the Beverly hillbillies discovered.

    Erica:
    00:06:15
    Exactly.

    Dan:
    00:06:15
    I was going to include that!

    Erica:
    00:06:19
    Yes.

    Dan:
    00:06:19
    Jed was out hunting for some food and up from the ground came a bubbling crude. That's it.

    Erica:
    00:06:27
    Oil that is.

    Dan:
    00:06:29
    Black gold.

    Erica:
    00:06:29
    Texas tea.

    Dan:
    00:06:30
    That's right. So that's that seep science. So today what we're going to do is we're going to talk about seep communities offshore because what I hope to be able to, you know, kind of convince you of is if oil and gas leak out of the sea floor, a seep community can form. Okay. Then we're going to talk about this thing called multibeam, which is a technique for mapping the sea floor because where you get a seep community, it affects the acoustic properties of the sea floor. And if we change the acoustic properties of sea floor or the shape of the sea floor with this mapping tool, we can identify a potential seep community and then we can go sample that.

    Dan:
    00:07:14
    And if we can sample it, we can analyze the geochemistry and the geochemistry will tell us whether or not we had oil or gas or both. And we can use it in all sorts of other ways. But that's where we're going to go to today. So that's kind of, that's kind of a map of our discussion today. Okay. So as Duncan said, most of the world, he Duncan talked about how in- if we have, an oil basin or gas basin with charge, there's going to be some leakage somewhere. And so the trick is to find that, okay. And so, we could, we could look at any basin in the world and we can look at where wells have been drilled and we can, we can look at where seeps leak out of the surface naturally. And there's a correlation, like for example, LA is a prolific hydrocarbon basin. Okay. And it has Labrea tar pits, one of the most charismatic seeps on earth cause you got saber tooth tigers bubbling out

    Duncan:
    00:08:18
    It's literally a tourist attraction.

    Dan:
    00:08:20
    Right there on Wilshire Boulevard. Okay. And it's a hundred meters long by 50 meters wide. So a hundred yards long, 50 yards wide. And it, that is an oil seep on, on the earth surface in LA okay.

    Duncan:
    00:08:32
    Now, it's important to mention that they're not all as big as that.

    Dan:
    00:08:34
    No, no. Sometimes they're smaller. It could just literally be a patch of oil staining in the sand.

    Erica:
    00:08:41
    Really, that's little.

    Duncan:
    00:08:41
    Oh yeah. I mean, or just an area where there's a cliff face with something draining out of it or it, you know, it could be really, really small, which is easy to find onshore. You know, you send the rover boys out there like you mentioned, and you know, geologists working on the ground, they're going to find these things eventually. But the challenge, which we've been working on with, with the guys from One for the last few years, and now is finding these things offshore.

    Dan:
    00:09:06
    So let's, let's turn the clock back to 1977. Alvin, a submarine, a submersible with three people in it went down on a Mid-ocean Ridge near the Galapagos Islands. And what they found, they were geologists going down to map where the oceanic crust is created. But what they found was this crazy community, this incredible, oasis of life with tube worms and these giant columns with what looked like black smoke spewing into the, into the ocean. And so what they found are what we now call black smokers or hot vents, and what was so shocking is the bottom of the ocean is it's a desert. There's no light, there's very little oxygen, there's not a lot of primary food energy. So what was this incredible, oasis of life doing thousands of meters down on, near the Galapagos Island? Well, it turns out that the base of the food chain for those hot vents are sulfide rich fluids, which come spewing out of the earth and they fuel a chemically based, community that thrives there and is an oasis as there because there's so much energy concentrated in those hot sulfide rich fluids that it can support these chemically based life forms.

    Dan:
    00:10:34
    So that's 1977 in 1985 in the same summer, chemically based life forms, but based on ambient temperature, water, not hot water were found in the Gulf of Mexico and off the coast of Oregon that same summer, 1985 in the Gulf of Mexico, the base of the food chain, what was fueling this chemical energy was hydrocarbons, oil and gas, and off the coast of Oregon, what was fueling it was hydrogen sulfide. So this is 1985, the year I graduated college. And so I started graduate school in 1986 and part of my research was working with the group that was trying to figure out the plumbing that was bringing these chemically rich fluids up to the earth's surface that were feeding this brand new community of life. You know, what we now call cold seeps. So, we, you know, depending on what you had for breakfast today, you know, eggs or pancakes or had your coffee, all the energy that we've got coursing through our veins right now is based upon photosynthesis.

    Dan:
    00:11:45
    We're either eating plants that got their energy from sunlight or we're eating eggs that came from chickens that eat the plants that can, where the came from, sunlight. Everything in our world up here is based upon photosynthesis. So, but the seep communities, the hot vents and the black smokers and the cold seeps, the base of the food pyramid is chemical energy. So they're called chemosynthetic communities or chemoautotrophic because the bacteria get their trophic energy, the energy that they need to live from chemicals. And so the bacteria utilize the chemicals and organisms have evolved to host these bacteria inside their bodies. And the bacteria metabolize the chemical energy to produce the enzymes that these larger organisms need to live. So these larger organisms can include clams, tube worms, the actual bacteria themselves. But, so the kind of how does this work is- let's get, because if we understand how seeps work and we know that seeps can be based upon oil and gas seepage, then you'll understand why we're using these seeps to go out and impact, oil and gas exploration.

    Dan:
    00:13:09
    So the- at the bottom of the ocean, we have a little bit of oxygen, but as we go down into the sediments, below the surface, we, we consume all that oxygen and we get to what's called the redox boundary to where we go from sulfate above it to hydrogen sulfide below it. And so below this redox boundary, we can have methane, we can have oil, but above that redox boundary, the methane will oxidize and the oil will be biodegraded and eaten by critters and whatnot. Now, living at that boundary, are bacteria who metabolize these compounds, and that's where they get the energy they need to live. These bac- Okay, now kind of turned the clock even farther back before the earth had an oxygen atmosphere, the only way that organisms got energy to live was from chemicals. Okay? So before we had algae and we created this oxygen atmosphere that we breathe billions of years ago, the organisms that lived on earth were chemosynthetic.

    Dan:
    00:14:13
    So these bacteria survive today and they live everywhere where we cross this redox boundary. Okay? So there they're actually archaea, which are some of the most primitive forms of bacteria, and I'm not a biologist, so I can't tell you how many billions of years ago they formed, but they're ancient and they're living down there.

    Erica:
    00:14:33
    So they haven't changed since then. They're basically the same?

    Dan:
    00:14:36
    Nope.

    Erica:
    00:14:36
    Wow.

    Dan:
    00:14:36
    They figured out a way to get energy to survive. It works.

    Erica:
    00:14:40
    Why change it?

    Dan:
    00:14:41
    If you're an Archea, right? So they're living down there at that redox boundary. Now, if we have seepage-seepage, is the flow of liquids. You actually lift that redox boundary. And if you have enough seepage, you can lift that boundary right to the sediment water interface. If you step in a pond and you smell that, sulfide, that rotten egg smell, your foot has gone through the redox boundary.

    Dan:
    00:15:08
    Okay? And you've disturbed some archaea down there and they'll get nudged aside. They'll go find someplace else. Okay? So with seepage, we lift the redox boundary to the sediment water interface and, and the bacteria are there and they're ready to utilize the reduced fluids as their source of energy. And so you can see them, we have pictures. You can do an internet search and say, you know, bacteria chemosynthetic bacteria and images and look at and look at photos of them. They it, they look like, okay, when you put the Guacamole in the back of the fridge and you forget it for three weeks and you open it up, that's what they look like. It's that fuzzy. It's this fuzzy mat of bacteria. And those are the bacteria. They're out there. They're metabolizing these fluids. Okay. Now in the process of metabolizing these fluids, they produce the bacteria, produce enzymes like ATP.

    Dan:
    00:16:01
    And I wish my partner John Decker, was here because he would correct me. I think it's adinase triphosphate and it's an enzyme that your body produces and sends out to basically transmit chemical energy. Okay. Now at some point in geologic time, and I'll, I'll actually put a number on this in a second. The larger fauna like clams and tube worms, evolve to take advantage of the fact that the bacteria are producing energy. And so they then evolve to use the bacteria within themselves to create the energy that they need to live. Okay? So, what happens is these seep fauna produce larva, the larva go into, you know, kind of a dormant stage and they're flowing around the ocean. And if they sense a seep, okay. They settle down and they start to grow and as, and then they, they, they, the bacteria become part of them.

    Dan:
    00:16:56
    They're the, the clams. You open a clam in the bacteria live in the gills. Okay. And so they'd grow and, and so these clams and tube worms start to grow and they form a community. Okay. So that a clam, what a clam does these clams, they stick their foot into the, into the sediment and they absorb the reduced fluids into their circulation system. They bring that, that circulating fluid to their gills where the bacteria then metabolize these reduced fluids and send the enzymes out to the tissues of the clam so it can grow. So this clam does not filter feed like every other clam on the planet. The tube worms that host these bacteria in them don't filter feed. So the base of the food chain is chemosynthetic. But the megafauna themselves, don't get their energy directly from methane or hydrogen sulfide. They get their energy from the bacteria, which in the bacteria, you know, the bacteria happy, they'll live anywhere.

    Dan:
    00:17:59
    But sitting here in a clam, they get the reduced fluids they need to live and they grow. Now it's what's cool for us as, as seep hunters is different species have evolved to kind of reflect different types of fluids. So if you know a little bit about seep biology, when you pick up like a batheum Modiolus mussel, you go, Huh? There could be oil here. Okay. Because that particular mussel is found in association with, with oil seeps. Okay. So that we won't go too far down that path, but there are different organisms. The important thing is that these communities, form again an oasis of life, a high concentration of life where we have a seep. Now, the oldest seep community that I'm aware of is Devonian. So that's between 420 and 360 million years. It's found in the high atlas mountains of Morocco.

    Dan:
    00:18:58
    And that seep community, a fossil seep community includes the same types of clams in tube worms that we find today. Okay. But they're also found with authigenic carbonate. Okay. Which is like limestone. And so, and that limestone in cases, this fossil seep community and has preserved it for hundreds of millions of years. So where does limestone come from? So remember we've got methane, CH4 in our, in some of our seep fluids. Well, if that's oxidized by bacteria, cause they're going to get energy from the methane they produced bicarbonate, which is HCO3 as a negative charge on it. And that bicarbonate, if it sees calcium, they like each other. And so they'll form calcium carbonate, limestone. And since sea water is everywhere saturated with calcium, if we have a natural gas seep, the bacteria will oxidize in natural gas and the bicarbonate will grab the calcium to form this cement.

    Dan:
    00:20:04
    Now deep enough in the ocean, it actually is acidic enough that that cement will start to dissolve. So we just have this, we have a factory of of bacteria. It might be dissolving some places, but most of the places we look, the carbonate doesn't dissolve. So we've got clams, tube worms, we've got the limestone authigenic carbonate, and if the pressure and temperature are in the right field, that methane can also form this really cool substance called gas hydrate and gas hydrate is a clathrate the, it's a combination of water and methane where the water forms an ice-like cage and the methane sits in that cage. And so you can light this on fire in your hand and the gas will burn. Nice yellow flame will go up from your hand and the cage will melt. The ice melts. So you get cold water on your hand with flames going up. It, it's cool stuff.

    Erica:
    00:21:03
    Did you bring one of these to show us today?

    Dan:
    00:21:06
    The pressure and temperature in this room are not, methane's not an equilibrium. You need hot, you need high pressure, moderately high pressure and you need very low temperatures. So, if we had-

    Duncan:
    00:21:20
    Neither are common in Houston, (Laughter)

    Dan:
    00:21:22
    No, and we wouldn't be terribly comfortable if that was what it was like here in this room. But the, the important thing for us now as we think about seep science and, and seep hunting is that this, this limestone cement, the authigenic carbonate, the gas hydrate, the shells of a clam, okay. Are All harder. Okay? Harder, I will knock on the table. They're harder than mud. So the sea floor, most of the most of the world's ocean is gray-green mud and ooze from all sorts of sediment and diatoms and plankton raining down onto the ocean floor. So most of the world's oceans is kind of just muddy sandy some places, but sediment, it's where you get these seep communities that now we've, we've formed a spot that some that's harder and rougher than the area around it. And that's our target when we, deploy technologies to go out and, and look at seeps.

    Dan:
    00:22:26
    So, so hot smokers, hot vents were discovered in 1977. Cold seeps were discovered in 1985 and were found to be associated, in the Gulf of Mexico with oil and gas seepage. That's 1985. Those were discovered with human beings in a sub in submersibles. Later, we deployed robotic submersibles to go look at seeps, ROV's and even later we developed tools to go sample seeps without needing to have eyes on the bottom and we'll come and talk and we'll come back and talk about that later.

    Dan:
    00:22:57
    But for kind of recap, a seep is a place where something is leaking out of the earth surface. When we talk about seeps, we're talking about offshore seepage of oil and gas that supports this profusion of chemically-based life forms as well as these precipitants, the authigenic carbonate limestone and gas hydrate. And the important thing is they change the acoustic properties of the sea floor.

    Duncan:
    00:23:28
    Yeah. Then the key thing is that you've gone from having, seeps onshore, which are relatively easy to walk up to and see, but hard to find, to seeps offshore, which are impossible to walk up to or very difficult. You need a submersible to do it. But because of this, chemosynthetic communities that build up around it and our knowledge of that and now gives us something to look for geophysically. So we can apply some geophysics, which we'll get on to talk about next in terms of the multibeam, to actually hunt for these things in a very cost effective way and a very fast manner. So we can cover, as Dan said, right at the start, hundreds of thousands of square kilometers, even over a million now, in a cost effective, timely manner and identify these seeps from the sea surface.

    Dan:
    00:24:15
    Now fishermen, know where seeps are because all of this limestone provides places for fish to leave their larva where they might live, they call them refugia. It's a, it's a place where, you know, lots of little fish and where you have lots of little fish, you have lots of big fish. And since we're also increasing this primary productivity, you get, you get profusions of fish around seep communities. So we've found authigenic carbonate in the front yards of fishermen in areas where that we've gone to study seeps. And if you chip a little bit off it, you can go and analyze it in the lab or if you can get somebody who fishes for a living to tell you their spots. And that involves convincing them that you're not going to steal their spots and you're not gonna tell everybody where their spots are. But if you go into a frontier area, if you can get somebody who fishes for a living to talk to you, you might have some ideas of where to go look for them.

    Dan:
    00:25:14
    So it kind of, one other point that I wanted to make here about seeps is, remember I talked about how seep organism creates kind of a larva, which is dormant and it's kind of flowing through the world's ocean, looking for a seep community, doing some back of the envelope calculations. If, if a larva can survive for about a month. Okay. And you have a one knot current that larva can move about 1300 kilometers in a month, which is about the length of the island of Java. And it might be about the length of the state of California. So if you think now, so if you think about that, then all you need is a seep community somewhere to be sending out larva. Most of which of course never gonna survive. And then if we get a seep somewhere else, the odds are that there's going to be a larva bouncing along the sea floor that is going to see that and start growing.

    Dan:
    00:26:08
    So for us as explorationists as the, the important thing is if there's a seep, there's a pretty good chance that, that a seep community will start to form, if the seepage lasts long enough, it will form a community depending, you know, might be large, might be medium size, but it changes the acoustic properties of the sea floor. Okay, so that, remember we're going to talk about seeps what they, what, what's a seep and that is how it's related to hydrocarbon seepage out of the or natural gas oil, you know, reduced fluids. What we were going to talk about, and now we're going to talk about how offshore we use this technology called multibeam to go and find them. Okay.

    Dan:
    00:26:51
    So back in, back in the Cold War, the air force came up with a tool to map the former Soviet Union called synthetic aperture radar. And when the navy saw the air forces maps, they said, we want a map of the sea floor. And at the time, you know, if you remember your World War II movies, the submarine sends out a Ping, somebody listening on, their, on their headphones and and the ping comes back and the amount of time that it took for the ping to go out and the ping come back is how deep the water is. If you know the speed of sound in water. But that's, that's just one point directly beneath you, that's not good enough to get a detailed map of the sea floor. So, driven by these cold war needs, the navy contracted a company called general instruments to develop a tool to map the sea floor and they develop what's called SASS, the sonar array sounding system, which we now call multibeam.

    Dan:
    00:27:49
    In the 1960s, it was unveiled to the world during a set of, submersible dives to the mid Ocean Ridge, I believe in 1975 as part of the famous project. And the geoscientist looked at that map and it was a contour map of the mid ocean region. They said, holy smokes, what's that? Where'd that come from? And the navy said, well, we kind of developed a new technology and it was first commercialized in 1977 the same year hot smokers were discovered on the world's oceans. And it has been continuously developed since then. And in about the 1990s, it got resolute enough for, for us to take this, this kind of seeps, seep hunting science and take it offshore. So until then, 1980s, we were deploying submersibles. We were going down and looking at them. We had very crude maps. We had some side scan shows, a little bit about, the acoustic properties of the sea floor.

    Dan:
    00:28:46
    But it wasn't until the mid 1990s that we realized that with these tools, these sea floor mapping tools that had acoustic, analyzing techniques that we could identify areas that were harder and rougher and had a different shape, that allowed us to start, instead of just driving around and, and, we're finding one by, by luck or chance actually saying, Huh, there's a, there's an interesting acoustic signature over there. Let's go take a look at it. And deploying submersibles and ROVs and realizing that yes, we had tools that could, be used to, to map the sea floor and identify seeps and driven by their own interests. The Navy, the US navy was very interested in these and, was, was a early, early funder of seep science and they've continued with it as well as academic institutions around the world that got very interested in seep communities.

    Dan:
    00:29:45
    And in fact, NASA, NASA is really interested in seep communities because they're chemically based life forms in what are basically extreme environments. And so if NASA wants to figure out what life is going to look like on a different planet, or a different moon on it, or surrounding a different planet that doesn't have an oxygen atmosphere, here's a, a laboratory on earth that, that they can use. So NASA has been funding seep science as well.

    Dan:
    00:30:11
    So multibeam what is it and how does it apply to, to, to hunting seeps. So multibeam, which is this technology that was developed by and funded by the navy in the 1960s and commercialized in the 70s uses two acoustic arrays of transducers. one array is mounted parallel to the length of a ship. And when you fire off all those transducers, it sends out a ping. And the longer the array is, the narrower that beam is. That's how antennas work. So that that long array sends out a ping, which is narrow along track and a shape, kind of like a saucer. So if you can imagine two dinner plates put together, that's what this, ping of energy looks like. And that's what we call the transmit beam. So then if you listen to the sea floor with an array that's perpendicular to the transmitter ray, we are now listening to an area that's, that's narrow across track. Okay. And it's long elongate a long track. So we've got this narrow transmit beam in one direction that's, that's now perpendicular to the ship. And we've got a narrow receive beam that's parallel to the ship and where those two intersect is what we call a beam. And so with, with lots of different, transducers mounted, perpendicular to the ship, we can listen from all the way out to the port about 65 degrees down below the ship and all the way over to starboard, again, about 65 degrees. And we have lots of beams.

    Dan:
    00:31:51
    So right now the system that we're using, on our project has 455 beams across track. So every time we send out a ping, we ensonify the sea floor on, on these 455 beams. And as we go along, we send out another ping and another ping. And we're basically, we're painting the sea floor. It's, it's like mowing the lawn with a big lawn mower or using a Zamboni to drive around an ice rink. You can just think of it as as a ship goes along. We are ensonifying and listening to a wide patch of sea floor and we typically map, about a five kilometer, about a three mile, a wide swath, and we send out a ping every six or 10 seconds. Depends how, you know, depends on the water depth. And so we're able to map 1000 or 2000 square kilometers a day with this technique. This multibeam technique.

    Duncan:
    00:32:48
    Since a lot of our podcast listeners might be familiar with seismic is that's probably the biggest percentage of the, the geophysical industry. This is not too different. It's an acoustic based technique. I guess the main difference is are we live working in a different, frequency bandwidth. And also that we have both the receiver and the transmitter both mounted on the same boat. So we're not dealing with a streamer out the back of a boat. we have transmitter and receiver are both whole mounted. But after that it's all pretty similar to seismic. We go backwards and forwards, either in 2D lines or in a, in a 3D grid and we build up a picture. Now because of the frequencies we're working with, we don't penetrate very deep into the sea floor. but as, as we mentioned, we're interested in seeing those seep communities on the sea floor. So that's why we this, this is the perfect technology for, for that application.

    Erica:
    00:33:40
    Oh, can you talk a little bit about the post-processing that's involved with multibeam?

    Dan:
    00:33:44
    Well, let me- Erica, Great question. Let me, come back to that later cause I want to pay, I want to pick up on what Duncan talked about in and add one very important wrinkle. So first of all, absolutely correct, the frequencies are different. In seismic, we're down in the hertz to tens of Hertz and in Multibeam we're in the tens of kilohertz and in very shallow water, maybe even over higher than a hundred kilohertz. In seismic, we have air guns that send that radiate out energy. And we, we designed the arrays so that we get most of the energy in the direction that we're looking with multi beam. We have a narrow, remember it's one degree wide in here. If you got kids, see if anybody still has a protractor anymore, grab a protractor and look at how wide one degree is. It's very narrow.

    Duncan:
    00:34:39
    There's probably an iPhone app for that. (Laughter) see what one used to look like.

    Dan:
    00:34:43
    But with, with seismic, the air guns sends out energy and we listened to the reflected energy out on the streamer back behind the ship or on a node somewhere else. It's reflected energy. With multibeam, the energy goes out and it interacts with the sea floor and the shallow subsurface. Most of it gets reflected away and we don't, we don't, hear that it, but some of it actually comes back in the same direction that the sound went out and we call that backscatter. So backscatter energy comes back to you and it's that backscatter that, can increase when we have hard and rough material either on the sea floor or buried below the sea floor. So the way that we process it is since we know the time of length, the time of path on how long it took to get out, hit the sea floor and come back, or you can correct for path lengths, energy radiates outward and spherical patterns. So we correct for spherical spreading. we know the angle that it hit the sea floor, so we correct for angle of ensonification. And then the next and most important things are where was the ship, when the pulse went out? And where is the ship when the pulse comes back, including what's the orientation of the ship? So we need to know the location, the position of the ship in X, Y, and Z to centimeters. And we need to know the orientation of the ship to tenths of a degree or better on both the transmit and the receive. But the key thing is, if we know that path length in the spherical spreading and we correct for all of that and we get a response that's much greater than we expected, we get higher backscatter energy and it's, it's those clams and tube worms authigenic carbonate gas hydrate that can increase the hardness and the roughness of the sea floor that kicked back the backscatter energy.

    Dan:
    00:36:46
    Okay. Now what happens if the oil and gas, or the reduced fluids if they shut off? Well, I'm sorry to say for the clams and the tube worms that they will eventually die. The bacteria will still live at that redox boundary as it settles back below the sediment. And then when we pile some sediment on top of that dead seep community, it's still there. The shells are there, the carbonate's still there. So with the, with multibeam that the frequencies, we use 12 and 30 kilohertz penetrate between two, three 10 meters or so into the sediment. So if you shut off the seepage and bury that seep community, they're still there. And if we can sample that below that redox boundary at that location, chances are we're going to get a oil or gas in, in our sample. And in fact, we encounter live seep communities very, very, very, very rarely, you know, kind of one in a thousand.

    Dan:
    00:37:50
    But, we, we encounter seep fauna down in our sample cores, which we'll talk about later, much more frequently. And, and we, we find hydrocarbons, we are very successful at finding hydrocarbons. And the key thing is we're using seep science to go look in, in basins or extend outward from basins in areas where there may be no known oil or gas production. And that's why the seeps are useful. So multibeam unlike a seismic, we got to collect the data, then we got it and you to do all sorts of processing and it takes a while to, to crank the computers and whatnot. Multibeam we can, we can look at it as it comes in and we can see the backscatter strength. We can see what the swath that it's mapping every ping, every six seconds. And it takes about, it takes less than a day to process a days worth of multibeam.

    Dan:
    00:38:47
    So when our ships are out there working every morning, when we get the daily report from the ship, we see another thousand or 2000 square kilometers of data that were mapped just the previous day. So it's for, those who can't wait, it's really satisfying. But for those of us who are trying to accelerate projects, it's great because when the data come off the ship, they're already processed. We can start picking targets and we can be out there, you know, in weeks sampling. So that's so multibeam it's, it's bathymetry, it's backscatter, but we're also imaging the water column. So if there's, a gas plume, coming out of the sea floor, naturally we can see that gas plume and, so that we can see the water column. We can see the sea floor or the bathymetry, and the backscatter. Erica, you asked, you know, about the processing and I talked about how we have to know the position and the orientation, of the ship, that means that we have to survey in using a laser theodolight.

    Dan:
    00:39:54
    We have to survey in every component of the system on the ship to, you know, fractions of a millimeter. And we drive the surveyors nuts because we are, we are more demanding than the, the BMW plant in South Carolina. And they point that out to us every time. Yes, we're more demanding. But if they have a problem with, with a robot in the BMW plant, they can go out and survey it again, once we put this ship in the water, I can't go survey the array that's now welded to the bottom of the ship. It's there. And so that's why we make them do three replicate surveys and do loop ties and convince us that we've got incredibly accurate and precise system. So that's when we survey the ship. We use, well we go back and we go and we check their math and we make sure all the numbers are entered into the system correctly.

    Dan:
    00:40:46
    We, measure the water column every day so that we have the best velocity data that we use to correct the, that position. We measure the salinity in the water column because it affects how energy is absorbed. It's called the absorption coefficient. We measure the acoustic properties of the ship. So we understand maybe we need to turn off the starboard side pump in order to get better multibeam data. And we evaluate every component of the ship. Something. Sometimes they'll have, you know, the, the waste unit was, was mounted onto the, onto the deck of the ship and nobody thought about putting a rubber bushing between that unit and the hall to isolate the sound. And it just so happens it's at 12 kilohertz. So it swamps your acoustic energy or degrades our data quality because it's all about data quality so that we can find these small, interesting high backscatter targets. We polish the hull. We send divers down every eight weeks or 12 weeks or 16 weeks because you get biofouling you get, you get these barnacles growing in a barnacle in between your acoustic array in the sea floor is going to affect the data. So we send divers down to go scrape the hull and scraped the prop.

    Duncan:
    00:42:05
    So it's probably worth mentioning that this is the same type of multibeam or multibeam data is the same data that is used in other parts of the oil and gas industry as well. So I mean, any pipeline that's ever been laid in the last few decades has had a multibeam survey before it. Any bit of marine infrastructure that an oil and gas company wants to put in the Gulf of Mexico. Certainly you have to have a multibeam survey ahead of time. what's different here is that we're, we're trying to cover big areas and we're trying to get a very specific resolution. So maybe it's worth talking a bit about that. Dan what we're actually trying to achieve in terms of the resolution to actually find seeps.

    Dan:
    00:42:42
    You got it. So we, we can, we can control the resolution because we can control how wide a swath we go and how fast we go. So, if you're really interested in, if you want to do a site survey and you want to get incredibly detailed data of a three kilometer by three kilometer square, you could deploy an autonomous underwater vehicle or an ROV and get very, very, very resolute, like smaller than half a meter of bin size. for what we do, where our goal is exploration, the trade off is between, do I want more resolute data or do I want more data and it that that is a tradeoff and it's something that we struggle with. And we think that the sweet spot is mapping that five kilometers swath and three miles wide, swath at about oh eight to 10 knots. So let's say about 16 kilometers an hour.

    Dan:
    00:43:40
    That gets us a thousand to 2000 square kilometers a day. And by acquiring data in that manner, we get a 15 meter bathymetric bin independent of water depth and our backscatter since we subsample that bathymetric bin for the backscatter, we can get a five meter backscatter pixel. So now if I have four, if I have four adjacent pixels, you know, shaped like a square, that's a 10 meter by 10 meter spot on the sea floor, it's slightly larger than this room. We could, you could see that now you might need a couple of more to be larger than that. So to have a target actually stand out, and that's about how accurate our sampling is with the core barrel. So, the long answer to your question is about a 15 meter bathymetric bin and a five meter backscatter pixel is what we're currently doing for our exploration work.

    Dan:
    00:44:32
    Now we pay attention to what's going on in the navigation and the positioning world because it affects our data quality. So the higher the quality of, of our navigation, the higher the quality of our data on the sea floor. So about a decade ago, the world's airlines asked if they could fly their airplanes closer together and the FAA responded and said, not unless you improve GPS and so sponsored by the world's airlines. They set up ground stations all in, in the, in the most heavily traveled parts of the world that improve the GPS signal by having an independent orbital corrections. What that means is for us working off shore, we take advantage of it. It's called wide area augmentation. And, using this system, which is now it's a, it's add on for a GPS receiver, we're able to get six centimeter accuracy of a ship that's out there in the ocean that surveying.

    Dan:
    00:45:27
    So that's six centimeters. What's that? About two and a half inches. And for those of us who grew up with low ran and very, you know, where you were lucky if you knew where you were to within, you know, a quarter of a mile. it's, it's just astonishing to me that this box can produce data of that quality, but that flows through to the quality of the data that we get on our surveys, which flows through to our ability to find targets. So I think, I told you about sub sampling, the bathymetry for backscatter and I've told, I told you about the water column and we've talked about the resolution. I think we've, we've pretty much hit what multibeam is. It's, it's a real time near real time acquisition, high frequency narrow beam. We image the sea floor and the shallow subsurface. Okay and we use that to find anomalous backscatter targets.

    Duncan:
    00:46:20
    Well, let's talk about the water column a little bit more done because I know we've published some pictures and images from our surveys. Showing the water column anomalies. The backscatter data, in the water column itself can actually help us find seeps. The right mixture of oil and gas coming out of this, an active seep and migrating up through the water column can actually be picked up on these multibeam data also. So that's, a real direct hit that you've got to see and that it's actually still producing oil today,

    Dan:
    00:46:53
    Right, so when, when gas and oil leak out of the sea floor, the gas bubble begins to expand as it comes up, just like a would in a, in a carbonated beverage because there's less pressure. So that gap, that bubble is expanding. If there's oil present, the oil coats the outside of the bubble and actually protects it from dissolving into the water column. And so the presence of gas with a little bit of oil leaking out of the sea floor creates these bubbles that, are big enough to see with these 12 and 30 kilohertz systems. And so when we see a plume coming out of the sea floor, that's natural, a seepage of gas, possibly with a little bit of oil and it provides a great target for us to go and hit. Now those seeps are flowing into the water column and the water column has currents and the currents aren't the same from one day to the next and one week to the next.

    Dan:
    00:47:47
    So if we image a seep a couple of different times, one day it will be flowing in one direction and the next time we see it flowing in a different direction. The area in common between the two is pointing us toward the origin point on the sea floor. And that's what we're going to target. And if you, if you hunt around, look for NOAA studies of, of the US Gulf of Mexico, over Mississippi Canyon near where the deep water horizon, went down because there are, the, NOAA has published, images of the gas seeps in that area where there are natural oil and gas seeps leaking, leaking other, the sea floor. And these natural seeps occur all over the world. Okay? And they're bringing oil and gas into the water column. But remember, nature has basically provided, the cleanup tool, which is the bacteria. So where oil and gas settle onto the sea floor, there are bacteria that will consume it. You don't want a lot of it in one place, cause then then you've got, you know, a real environmental disaster. But natural oil and gas seepage goes hand in hand with natural seep consuming organisms that metabolize these fluids. So a multi beam seeps backscatter okay. That I think we've, we've talked about what the target looks like. Let's talk about how we go in and sample it.

    Duncan:
    00:49:12
    Yeah, no, I think that's the real key thing. Particularly here in the Gulf of Mexico. I mean we talked at the start about how I'm using seeps can tell you whether a basin has hydrocarbons in it or not. Clearly we're decades past the point of knowing whether there's oil and gas in the Gulf of Mexico. So even in the deep water gulf of Mexico, especially here in the US side, we know that there's oil and gas, so that information is long gone. We don't, we don't need an update on that anymore. What we need to know is information about the type of oil, the age of the oil, the deep positional environment that the oil is deposited in. And if we can actually get a sample from these seeps, then that's the sort of information that modern geochemistry can start to pull out for us.

    Dan:
    00:49:57
    we've sat in the same meetings where the, the potential client companies have said, why are you, why are you gonna map the deepest part of the Gulf of Mexico? There's no oil out there. And lo and behold, we found anomalous backscatter targets on a diapirs, which are areas, mounds out in the deepest parts of the Gulf of Mexico. And lo and behold, if you, if you look at the data, know that that statement was incorrect. There is oil and gas out there in other parts of the world. We've had companies say, oh, this part's all oil and this part's gas. Well, how do you know that? Well, because we've drilled for oil out here and we don't think there's any oil. Once you get out there and you don't know, you don't know what you don't know until you go map it and sample it and then you come back, you put the data on their desk and they go, huh, hey, we were wrong man. I guess there's oil out there. And, and in other parts of the world where you know, we've done all our exploration close to land or in shallow water, we go out into the deepest part and nobody's ever drilled a well out there. So, you use the seep science to go to basically fill that in.

    Dan:
    00:51:09
    So in order to make money exploring for oil, you had to have organic matter. Originally it had to be, it had to be buried and cooked. Okay. So you needed temperature and pressure. You need time takes time to do that, then it needs to migrate. Okay. With the exception of unconventionals, we're not gonna talk about unconventional today with the exception of unconventionals, the hydrocarbons have to migrate, so they're concentrated so that you can go drill them and recover them. And they need to be in a reservoir.

    Dan:
    00:51:41
    And it has to be sealed. And so when we find a seep and all of that goes into what we talk about in oil exploration as the risk equation, like what's the probability of success? If you don't know whether you have a migration, you have maximum uncertainty and that flows through into your, into your risk. Well, if we find a seep, remember we've proven that there was organic matter. We've proven that it was buried and cooked for the right amount of time to create oil and gas and that it's migrated. We can't tell you anything about reservoir or seal or timing, but we can, we can materially impact the risk equation by finding a seep. Okay. So right before you drill a well, wouldn't you like to know whether or not there's oil or gas in the neighborhood? Cause a well can be a can be $100 million risk.

    Dan:
    00:52:34
    Okay. Usually you wouldn't, wouldn't you like to know? So remember when we started looking at seeps, 1977 for the hot vents 85 for the cold vents, we used human beings in a submersible. Later we shifted to using robotic submersibles where a human being sit on a ship in a control room, operate the ROV with joysticks, and you watch the videos come through. Well, those are great, but they're really expensive and you can't look at much sea floor on any given day because you're limited to how fast you can move across the sea floor and how much you can look at. So if we surveyed 2000 square kilometers in a day, we want to be able to evaluate that in less than 20 years. We want to be able to evaluate that in, you know, in a similar length of time, a day or two. So what we've done is we've shifted toward using what we, what's called a piston core, which, which is a six meter long, 20 foot long tube with about a thousand kilos on a 2,000 pounds.

    Dan:
    00:53:37
    And we lower it through the sea floor, operating it with a winch from a ship. And by putting a navigation beacon on that core, we can track it through the water column in real time. And if we have this high backscatter target on the sea floor, we can lower it to the water column. Once we're about fit and we're within 50 meters, 150 feet of the sea floor, we can see whether we're on target and then we let it go. When the pist- when the, it has a trigger weight on it, you can look this up, how to, how do piston cores work, that the core, lets go and it free falls that last little bit and it penetrates the sea floor. You haul it back to the surface. Now if it had gas hydrate in it, if it has oil in it, if it has gas in it, you can see it right away. when you pull the clear liner out of the core, and there it is, you know, whether or not you've got success, for most cores, there's no visual evidence of hydrocarbons that we sample that core tube, three different samples. One of them, we take a sample into what we call a gas can and seal that. And then we put a couple of hockey puck size chunks of sediment into Ziploc bags and everything goes into the freezer. And you ship that back, from the next port call. And about a month later you get a spreadsheet in your email, that says, oh, guess what you found methane, ethane, propane, butane, and Pentane. And look at this, you've got enough fluorescents that this is a guaranteed oil hit. So, again, you think about the time we map a couple thousand square kilometers a day.

    Dan:
    00:55:18
    We mapped for a month, we'll look the data for a month. We go out and core for a couple of weeks and a month later the Geochemistry starts flowing in. So real quick, multibeam as we've, as we've discussed as a way to get a detailed map of the sea floor, both the shape of it and the hardest roughness, acoustic properties. So any company laying a fiber optic cable across the world's oceans is acquiring multibeam data. Any, municipality that's worried about how deep their ports are and whether there's enough space for the ships to come in, is acquiring multibeam data. The corps of engineers who pays companies to dredge sand in the Mississippi River has to have a before and after multibeam a map, when MH370 went down and needed to be hunted for before they deployed the real high resolution tools. They needed a map of the sea floor and that was a part of the ocean that has never been mapped in detail before.

    Dan:
    00:56:23
    So most of the world's oceans have net have never been mapped in the detail that we're mapping them. We're using the tool to go hunt seeps. But there are all sorts of other uses of, of that multi beam technology. So, what are we looking for when we, when we, when we're looking for seeps, you know, what have, where have people found oil and gas leaking out of the sea floor? What does it look like? Or what are the targets? Well, if the gas burps out of the sea floor, it creates a pockmark. And those are targets, in many parts of the world, the Apennines of Italy, Azerbaijan, there are what we call mud volcanoes, where over pressured mud from deep down in the earth is kind of spewing out gently, slowly and continuously at the earth's surface. And lo and behold, it's bringing up oil and gas along with it. So mud volcanoes are known, oil and gas seeps onshore. Of course we're going to use them, offshore. Any place where we have a fault, you can create fracture permeability that might let oil and gas up. Faults can also seal, but a fault would be a good target, an anticline, a big fold that has a, can have seeps coming out of the crest of, it's similar to the seeps that were discovered early in late 18 hundreds. And in, in the USA, we can have areas where we have oil and gas leaking out of the sea floor, but it's not enough to change the shape of the sea floor. So we get high backscatter but no relief. Those, those are targets. So when we go out and we sample potential seep targets, we don't focus on only one type of target because that might only tell you one thing.

    Dan:
    00:58:04
    So we spread our, our targets around on different target types and we'll spread our targets around an area. Even if we, if we have more targets in one area than another area, we will spread our targets all the way around. Because the one thing that we've learned in decades of seep hunting is we're not as smart as we think we are. Nature always throws a curve ball. And you should, you should not think that you knew, know everything before you go into an area to analyze it because you might, you probably will find something that's, that startles you. And you know, as someone who's been looking at seeps since 1986, I continue to find things that we've never seen before. like our recent projects in the Gulf of Mexico, we found two target types that we've never seen before. The nearest analog on earth, on the surface is called a Pingo, which is when ice forms these really weird mountains up in the Arctic. And the one thing I can guarantee you that's not on the bottom of the world's ocean is an ice mound similar to what's forming the Arctic. But, but it had that shape. So we went and analyzed it and lo and behold, it told us something about the hydrocarbon system.

    Dan:
    00:59:12
    So those are all different types of target types so that the core comes back, we send it to the lab, we get first the very, what call the screening geochemistry, which is a light gases, methane through Pentane. We look at how fluorescent it is, cause that'll tell you whether or not you, you have a chance of of having a big oil hit. And we also look at what's called the chromatogram, which is a gas chromatography. And that tells us between about C15 and C36 C being the carbon length. So the, all your alkanes. And by looking at a Chromatogram, a trained professional will look that and say, oh, that's biodegraded oil. Or, oh, that's really fresh oil cause really fresh oil. All the, alkane peaks get smaller as they get bigger. So it has a very, very distinctive shape. Or they can look at it and they can tell you, you can, you can figure out the depositional environment. You can figure out whether the organic matter came from a lake, lacustrine, or maybe it's marine algal. We can say something about the age of it because flowering plants didn't evolve on earth till about the end of the age of dinosaurs. So at the end of the cretaceous, we got flowering plants. And so flowering plants create a molecule called oleanane. And so if there's no oleanane in the oil, that oil is older than cretaceous. So now we're telling something about a depositional environment.

    Dan:
    01:00:39
    We're saying something about the age, we can say the, the geochemist can say something about the maturity of the oil by looking at the geochemistry data. So all of this information, is now expanding what we know about what's in the subsurface and everything we know about seepage is that it is episodic in time. And it is distributed on earth's surface, not in kind of a random scattered, fashion. You get seepage above above a mud mud volcano, but for the surrounding hundred square kilometers around this mud volcano, we don't find any seep targets. Okay. So, our philosophy is that in order to find, in order to analyze the seats, we have to go find where we've got the highest probability of seepage and leakage. And that's where we target. So if you went out and just dropped a random grid over an area, you have a very, very low chance of hitting a concentrated site of seepage. And so, our hit rate, our success rate is, is high because we're using these biological and chemical indicators of seepage to help us guide where we sample. We have very precisely located sampling instruments this core with this acoustic beacon on it. And so we have, we have a very, very high success rates. And when we get hydrocarbons, we get enough hydrocarbons that we can do all of this advanced geochemistry on it.

    Duncan:
    01:02:13
    That's a good point Dan, even with- even without just doing a random grid of coring, piston coring has been done in the the US Gulf of Mexico for a long time now. And using seismic information, to target it. So like you say, looking for the faults and the anticlines and those type of features and very shallow anomalies on the seismic data. Even even guiding it with that information, typically a, a 5% hit rate might be expected. So you take two or 300 cores you know, you're going to get maybe 5%-10% hit rate, where you can actually look at the oils, and the geochemistry from the samples that you get. Using the multibeam, we were more like a 50 to 60% hit rate. And that's even with like Dan said, we're targeting some features where we know we're not going to find oil. so we could probably do even better than that if we, if we really focused in on finding oil. But obviously we're trying to assemble all the different types of seeps.

    Dan:
    01:03:11
    One of the things that we're asked and that we've heard from managers since we started working in the oil industry is what is this sea floor seep tell me about what's in my reservoir. And there's only, there have been very few, what we, what we call the holy grail studies published where a company has published the geochemistry at the reservoir level and the geochemistry on a seep that they can tie to that reservoir in the Gulf of Mexico. We collected dozens of seeps that can be tied to the same basin where there is known production. So in that Gulf of Mexico Dataset, a company that purchased that data and who had access to the reservoir oils could finally have a sufficient number of correlations that they could answer that question. What is the sea floor seep? Tell me about the reservoir. Because once you're comfortable in the Gulf of Mexico, that that seep is really telling you what's down in your reservoir.

    Dan:
    01:04:08
    Now you go into other parts of the world where you don't know what's in the reservoir before you drill and you find a good, a fresh seep with fresh oil right at the sea floor. Now you're confident that when you go down into the reservoir that you're going to find something, something similar. So let me talk a little bit about other things that you can do with these cores. And I'll start by kind of looking at these mud volcanoes. So this mud volcano, it had over pressured mud at depth. It came up to the surface of the earth and as it came up, it grabbed wall rock on its way up. So by analyzing a mud volcano, if we then go look at, say the microfossils, in all the class in a mud volcano, we can tell you about the age of the rocks that mud volcano came through without ever drilling a well.

    Dan:
    01:04:54
    So you can look at, at the, at the vitrinite reflectance, you can look at the maturity of the, of these wall rocks that are brought to you on the surface. You can look at heavy minerals. And when we go out and we do field geology, you know, you remember you're a geologist has a rock pick they and they go, the geologist goes up to the cliff and, and she or he chips a rock out and they take it back to lab and take a look at it. And that's how they tell something about what's in the outcrop. Well, it's hard to do field geology on the bottom of the ocean using a multibeam map and - acoustically guided core. We can now go and do field work on the, on the ocean floor and expand our knowledge of what's going on in a field area.

    Duncan:
    01:05:42
    So maybe it's worth talking a bit Dan about how we're jointly using these technologies or this group of technologies, at TGS, to put together projects. So the, I think generally the approach has been to look at, basin wide study areas. So we're not just carving off little blocks and doing, one of these, one of these projects over, over a particular block. We'll take on the whole Gulf of Mexico. So we, we broke it up into two. We looked at the Mexico side and the US side. But in total, I think it was nearly a million square kilometers that we covered and, about 1500 cores that I think we took, so we were putting these packages together in different basins all over the world, whether they're in mature basins like the Gulf of Mexico or frontier areas like places we're working in West Africa at the moment. But I think we're, we're looking to put more and more of these projects together. I think the technology applies to lots of different parts of the world. Both this side of the Atlantic and the eastern side of the Atlantic as well.

    Dan:
    01:06:44
    So since 2014, five years, we've mapped, we as in One and TGS have mapped, I believe over 1,250,000 square kilometers. We've acquired over 2000 cores. Oh. We also measure heat flow. We can use - is how the earth is shedding heat. And it's concentrated in some areas in, and you want to know heat flow if you're looking for oil, cause you got to know how much your organic matter has been cooked. So we've, we've collected thousands of cores, at dramatic success rates and we've used them. We've used these projects in areas of known hydrocarbon production, like the shallow water Gulf of Mexico, but we've, we've extended out into areas of completely unknown hydrocarbon production, the deep water Gulf of Mexico, the east coast of Mexico over in the Caribbean. We're looking at northwest Africa, Senegal, The Gambia, Guinea-Bissau, and the area, that's a jointly operated AGC. And we're looking at other frontier areas where we can apply this to this technology in concert with traditional tools, multichannel, seismic, gravity and magnetics to help, our clients get a better feel for the hydrocarbon prospectivity. You've got to have the seismic cause you've got to see what the subsurface looks like. But the, the multibeam which leads to seep targets, which leads ultimately to the geochemistry is what then affects the risk going forward into a basin.

    Duncan:
    01:08:20
    That's a good point, Dan. We don't see this as a technology that replaces seismic or gravity or magnetics or anything else, but it's another piece in the puzzle. And it's a very complimentary piece as well.

    Dan:
    01:08:31
    It is. And any areas you could argue that probably the best places to go look are where, your colleagues and other companies have said, oh, there's no oil there. Well, how do you know? Well, we don't think there's oil because we don't think there was a organic matter or we don't think that it was cooked enough. Well, you don't know until you go there and you find, so if you found one seep in that field area that had live oil and gas in it, you would know that that premise was incorrect. And now you have a competitive edge, you have knowledge that others don't and that can, that can affect your exploration, strategy in your portfolio. we haven't talked about cost. Multi beam is arguably one of the least expensive tools per square kilometer in the geophysical toolkit. Just because we don't need chase boats. We're not towing the streamer, we're going 10 knots. We're covering a couple of thousand square kilometers a day. So it's, it's, it's a tool that's useful in frontier exploration. It is complimentary to seismic, and it's a tool that, that you can use to guide where you want to spend money and how much money if you, if we survey a huge area and let's say half of it has no evidence of oil and gas and half of it has excellent hydrocarbon seeps, both oil and gas. I would argue that as a company you might want to spend less money on the first and more money on the second. You might not want to spend zero because there's always a chance you might want to take a look at it. But this tool is, we're kind of the leading edge of the sphere that can be used to go in and open up a new basin, and see whether or not there's oil, there's oil and gas there.

    Dan:
    01:10:21
    Well Erica and Duncan, thanks for having me today.

    Erica:
    01:10:28
    Our pleasure.

    Dan:
    01:10:29
    It's been an interesting discussing arcane subjects like bacteria, seep organisms and sea floor mapping and multibeam and tying it all back into oil and gas exploration. I want to take a minute to thank TGS for this podcast and for, their collaboration and support over the years. And I should mention that we have, we've been acquiring the seafloor mapping data with, companies like Fugro and TDI Brooks with equipment, designed by Kongsberg. I want to thank my partners in One, John Decker and Phil Tees. And on the geochemistry side, I've got to give a nod to Bernie Bernard for, patiently answering my questions and our colleagues over at GeoMark for the biomarker work. For biology, Chuck Fisher has again, patiently answered all of my questions about how seeps work and for Paleo seeps, Kathy Campbell. And I do want to thank a the US Navy's office of naval research for decades of sponsoring this type of work, and, and allowing us as a, as a kind of an academic community to take these really, again, arcane science issues. But then to take them and apply them to, the really exciting work of oil and gas exploration.

    Duncan:
    01:11:42
    Thanks for coming by Dan. It's always a pleasure.

    Erica:
    01:11:46
    Thank you both. It's been a great conversation

    Dan:
    01:11:48
    And who knows where we're going to map next...

    2019 Summer Internship Program: So much more than coffee

    2019 Summer Internship Program: So much more than coffee

    In this episode of Beneath the Subsurface we introduce our Geoscience and Data & Analytics intern teams for our summer internship program. Erica kicks off the episode with Jason and Sri talking about how the programs have come about and changed overtime here at TGS, how they select and recruit for the program, and the scope of the projects that the internships tackle this summer. Erica then spends time with both teams of interns discussing the experience in the program, what they’ve learned, and everything they’ll be taking away and applying back to their studies and upcoming careers.

     

    TABLE OF CONTENTS
    00:00 - Intro
    00:50 - Team Leader Segment with Jason and Sri
    01:09 - The Geoscience Internship Program
    04:42 - The Data & Analytics Internship Program
    07:29 - Advice for Program Applicants
    11:54 - Data & Analytics Intern Team Introductions
    13:32 - The D&A Summer Projects
    15:18 - Lessons Learned Pt. 1
    17:20 - The TGS Internship Experience Pt. 1
    20:24 - Future Careers
    21:41 - Advice for Future Interns & Reasons to Apply Pt. 1
    24:34 - Valuable Take Aways Pt. 1
    26:01 - Geoscience Intern Team Introductions
    28:36 - The Geoscience Summer Projects
    31:33 - Lessons Learned Pt. 2
    33:14 - The TGS Internship Experience Pt. 2
    34:12 - Advice for Future Interns & Reasons to Apply Pt. 2
    39:28 - Valuable Take Aways Pt. 2

    EXPLORE MORE FROM THE EPISODE

    EPISODE TRANSCRIPT
    Erica Conedera:
    00:12
    Hello and welcome to Beneath the Subsurface a podcast that explores the intersection of geoscience and technology. From the Software Development Department here at TGS, I'm your host, Erica Conedera. This time around, we'll be chatting with our newest batch of intrepid students in TGS' dynamic and immersive internship program. As you will hear, they are a diverse group of future innovators from around the world. They bring with them a wide range of skills and interests and work together to collaborate on exciting real world projects. We'll start our conversation today with a quick introduction from the leaders of our internship program. I'm here with Sri Kainkarayam, the data science lead and Jason Kegel with the geoscience team who heads up the geoscience intern program. And we're going to talk a little bit about the internship programs. Jason, how has this program changed in the last five years?

    Jason Kegel:01:09
    When we first started the program, I want to say 2013, 2014, it was out of the Calgary office in Canada. The interns there were mainly from some of our Calgary schools nearby. And then it started to grow 2014, 2015 to include some of our Texas schools, UT, Baylor, University of Houston. As it's grown, we've decided to add more projects and more sort of interesting work to the projects. We've also been able to bring on some of our original interns into roles within the company. So over the last five years, I'd say the biggest thing that's grown is the, the number of interns. So in Calgary, when this first started we had one intern and then that same intern came back a second year and we brought another one on. And then we got one in Houston. And then as that grew, we had a couple in Houston and a couple in Calgary.

    Jason: 02:09
    And then the past couple of years we've had four each year. So we had four last year and four this year. So we've really been able to sort of guide new projects around that to where we can really include their schoolwork and what they're doing in their university work with what we're doing here at TGS and hopefully build a sort of cohesive project for them to work on. And that's sort of the struggle with a lot of internship projects that we've done over the past years is to incorporate what they want to do as students and as interns and as their career grows, with what we'd like to see them do and encourage them to do within TGS.

    Erica:
    02:49
    Does that go into the consideration of which interns you end up picking, what their specialties are or what they're looking to do with what you need?

    Jason:
    02:58
    No, not necessarily, a lot of the times the interns, so for example, last year we were working very closely with a couple of schools that we wanted to bring data into. So some of our production data our Longbow group into with the University of Lafayette. So we were working really closely with a few professors out of that school and a few professors with UH. So we had recommendations from the professors themselves with students that they thought might work nicely with us with - in terms of their knowledge of data already and their knowledge of well log use and seismic, so they can kind of jump in running without having to learn too much in the beginning, without too much of a learning curve. So in aspects of that, and that's, that's more that we look for. So the, the professors we're working with, along with how long it will take them to, to get up and running with things.

    Jason:
    03:51
    Our current group of students is sort of a more advanced set of students who are working on their PhDs or in their later years of their master's degrees. So they've already seen a lot of these areas and worked with a lot of the data. So we do look for sort of more advanced students now, whereas when we first started the program, we were, we were happy to get anybody, some people that were not sure if they were going to be geoscientists, but you know, we're in the geoscience program with their bachelor's and that was okay too. I think we still got a lot out of having them here, working with us. but as we've grown, we've been putting them on more and more advanced projects and they've really been able to help out.

    Erica:
    04:29
    Cool, sounds like they've added a lot of value.

    Jason:
    04:30
    They definitely do. And it's nice to have sort of fresh faces around in the summertime and, and it really, really fills in for everybody that goes on vacation in the summer.

    Erica:
    04:39
    (Laughter) Right? Awesome.

    Jason:
    04:39
    The office doesn't seem so empty.

    Erica:
    04:42
    Awesome. So for the data analytics team, the internship program is new. I think this is your first batch of interns, correct Sri?

    Sri Kainkaryam:
    04:57
    Yes. So the data science team started sometime around November, 2017 so this is, although this has been our second summer, this is our first batch of interns that are projects, both, trying to test out novel algorithms, novel approaches, also try and apply ideas from high performance computing to building workflows, and also try and build sort of, user interfaces or ability to, deploy these for various users. So, there are broadly three buckets in which these projects fall into. And, it's an, it's, it was an interesting time looking for an intern because data science as, as a domain is, sits at the intersection of sort of three, broadly non intersecting sets, right? So geoscience, computing as well as machine learning or deep learning and folks having adequate background in all three of them, they sort of fit the -the mold of a good intern.

    Sri:
    06:02
    So it was in some sense was a little hard initially to try and find an intern. So I think we have a talented group of interns working on two of the broad offerings that we have right now. One of them is Salt Net, that is trying to interpret salt bodies from seismic images, and one is called ARLAS that is curve completion and aspects of petrophysics that can be done on, on wells that are available in an entire basin. So, it's, it's been four weeks into the internship program and the interns, the interns are pretty smart. They're motivated and it's been a fun experience so far.

    Erica:
    06:43
    Is it a 12 week program in total?

    Sri:
    06:46
    It's around a 12 week program. Some of them I think are here for a little longer than that. So, one of them is, trying to build a tensorflow port of our salt network flow because tensorflow community comes with a bunch of advantages such as, like, ability to deploy, it also comes with a JavaScript library called tensorflow JS that that makes it easy to do machine learning in the browser. So we want to make use of that infrastructure and the community built infrastructure. And that's one of the reasons why, one of the interns is spending time trying to build, trying to put our workflow in onto tensorflow.

    Erica:
    07:29
    So if you guys had some advice to give to people looking to get into the internship program, would you have anything you'd want to let them know?

    Sri:
    07:37
    So from the perspective of data science internships, given that how fast the field is moving, especially for students looking for data science internships in, in the space of oil and gas, the first and foremost thing is having an ability to understand various aspects, various various sources of data or aspects of data in the upstream domain. Because, just to give you an example, somebody who's worked on deep learning of natural images throughout, the moment you try and apply similar algorithms onto seismic images, it's a completely different domain. So, what are the, what are some of the assumptions that you can make? And that's where having a strong domain background really helps.

    Sri:
    08:30
    And I think the second thing that is, that's becoming very important in the marketplace right now is, is with, with platforms like GitHub or, you know, various open source projects. You can actually showcase your code. So pick a problem, learn a few, learn some approaches or try out some novel approaches, and put out the code out there. Put that on your resume because that adds a lot of weight, in your, in your ability to make a case for an internship rather than somebody who hasn't, who says, oh, I have, I have a strong programming background, but there's no way for somebody who's evaluating the person to see the code. So that these days has become a really strong advantage for, for a lot of students. So a couple of the students that are working with us this summer, they actually have active GitHub profiles where they've posted code, they've contributed code, various projects and so on. And as a consequence, like we looked at their profiles and backgrounds and like, oh, this is an obvious fit to our group and this person also has a background. A couple of them were like Ph.D students in geophysics, so it's an obvious fit for our team. So it was, it was all, it was a no-brainer for us to get them to come work with us this summer,

    Erica:
    09:53
    Jason?

    Jason:
    09:53
    On the geoscience side, it's, it's quite a bit different really. A lot of the students that are in university going for, for geoscience and wanting to go into the oil and gas industry have mainly just academic experience. So we really just want somebody that can sort of get up to speed quickly with sort of what an explorationist in an oil and gas company would do is look at essentially what we're bringing them in to do is what a sort of a mini, really quick exploration studies on basins where they don't have to go full on to drill a well, but they still need to have the ideas behind it where they can use the data, they have to evaluate an area and come up to speed quickly with, with getting those presentations out. So having really good presentation skills and having just a background enough to be able to learn on their own and pick up concepts quickly really helps. We see that a lot with, since we do get a lot of our interns through their advisors at different universities, that that really helps. But it also doesn't hinder it. We've also had lots of students that have applied, that have came from different universities where we don't know the advisors and it's just a matter of them going through the interview process and showcasing that they're, they're able to get to speed quickly. So, anybody can really go, go and do this type of work if they have the, the ability to learn.

    Erica:
    11:14
    Awesome.

    Sri:
    11:14
    I think that's an interesting point that Jason brought up. The ability to learn things fast and, sort of the ability to, appreciate various data sets and trying to understand and bring them together. I think that's a huge advantage for, for students. And based on my interaction with students in our group as well as Jason's group, I think TGS this summer has a fabulous group of interns.

    Erica:
    11:43
    Okay. Well thank you guys for talking to us about the internship program and we're very happy to talk to your respective groups and see what they have to say. Thank you.

    Sri:
    11:52
    Thank very much.

    Jason:
    11:53
    Thank you.

    Erica:
    11:56
    I'm sitting here with our first group of interns from the data and analytics group. To my left, we have Michael Turek from Florida State University. His major is computer science. He has a B.S. In computer science as an Undergrad. What are your career goals? What are you working towards?

    Michael Turek:
    12:15
    Yes. So part of me taking an internship here at TGS was to help figure that out. And so, well, you know, my interests rely mostly in machine learning and things like this. So something pretty, along those lines.

    Erica:
    12:31
    Awesome. Well we hope you, we'll help you figure that out. While you're here. Going around the table, we have Lingxiao Jia from the University of Wyoming. Your major is geophysics and you're working towards your PhD studying seismic imaging, migration and inversion. What kind of career are you working towards?

    Lingxiao Jia:
    12:50
    I plan to work as a Geoscientist in the oil and gas industry.

    Erica:
    12:56
    Awesome.

    Lingxiao:
    12:56
    Yeah, I like to do programming, so mostly on that.

    Erica:
    13:06
    Cool. All right. And then to my right, we had Deepthi Sen, from Texas A&M, majoring in petroleum engineering, working towards your PhD, studying reservoir engineering. What's your career goal, Ms. Deepthi?

    Deepthi Sen:
    13:21
    I'd like to, get a full time employment in the oil industry, preferably working on something related to machine learning in reservoir engineering. So yeah, that's why one of the reasons why I'm here too.

    Erica:
    13:33
    Awesome. Yeah. Oh, we're glad all of you are here. So can you guys describe for us, the projects you're working on? I'm not sure if you guys are all working on the same project or if you're working on different projects.

    Deepthi:
    13:45
    We are working on different projects. So right now I'm working on something which, involves clustering well logs, into good and bad, sections.

    Deepthi:
    13:57
    I use machine learning and a few algorithms that I use for my graduate research too.

    Erica:
    14:04
    Very cool. What's a bad section?

    Deepthi:
    14:07
    A bad section as in, there are certain depths at which, certain well logs behave erratically so we want, do not want to use that data, so we have to cluster it out. So, in order to do that manually for, you know, thousands of wells, it's impossible. So that's where machine learning comes into play.

    Erica:
    14:27
    Very cool. Very useful too. Lingxiao?

    Lingxiao:
    14:32
    I'll be working on using machine learning to do the recognition of geoscience features. For example, there could be faults, it could be picking horizons, could be recognizing salt domes, something like that.

    Erica:
    14:48
    Wow. Very complex and over my head. (Laughter) I'm sure it's very important though. And you, sir?

    Michael:
    14:57
    Yeah, so I'm working on translating the models that TGS' data analytics team uses to predict salt patches in the earth. So they use, they use models written in a module called Pi Torch and I'm converting that to tensorflow 2.0

    Erica:
    15:17
    Cool. Very cool. So what have you guys learned along the way so far? I know this is kind of the beginning for you, but-

    Michael:
    15:28
    Yeah, so it's, it's somewhat difficult to- so much, is kind of the answer to that question. But a lot of what I've learned boils down to more of the theory side of machine learning. Coming into the internship I didn't know a whole lot about the backend of machine learning, mostly just applying it. So learning how all these models work and why they work and things like that in terms of, the actual actually applying machine learning. That's what I've learned. I've also learned though, perhaps more importantly, working with a team and collaborating and things like that, which has been-

    Erica:
    16:10
    So hands on, real-world experience. What do you guys say to that? Ladies, I should say (Laughter) to my right.

    Deepthi:
    16:17
    So as I said, the research that I do is again, on machine learning. So I get to use similar algorithms to another, I would say facet of oil and gas. So I worked in reservoir engineering back in Grad school. Here I'm working on, petrophysics, so I kind of see how the same algorithms and same concepts can be applied in two different, areas, which is quite eye opening. Yeah. And apart from that I'm learning new algorithms and learning new math, which, I would think that's very important for, for my Grad school too, so, one good thing about TGS is that, they are quite, you know, they don't mind, publishing. So as a PhD student, that's very important to me. So that's one thing I look forward to too.

    Erica:
    17:08
    Yeah. Awesome.

    Lingxiao:
    17:10
    For me, it has helped me get a deeper understanding of how much, how machine learning works and how it could be applied to the field of Geo Sciences.

    Erica:
    17:20
    Cool. So talking about TGS more broadly, like as a culture, how would you say it's like working here, if someone were to ask you from school, what's it like working at TGS? What's that company like? What would you say?

    Deephti:
    17:36
    It's a very friendly atmosphere and, it is different from Grad School, in the sense that, I think Grad School, hours are more flexible than in an industry environment. But then, the focus is different and this is more, you know, I would think this more social than Grad school and, you know, being here, this is my first internship in the US, the environment is very friendly and you know, people look out for each other it's great.

    Erica:
    18:15
    Cool.

    Lingxiao:
    18:15
    Yeah. People here are so helpful and the, I have had a great time. I really enjoy this internship by far. Yeah.

    Erica:
    18:26
    Awesome.

    Michael:
    18:26
    It's wonderful. You're working in small teams and so you get to know everyone pretty well. It's very tight knit and those people are smart and very helpful kind people. It's, it's, it's wonderful.

    Erica:
    18:37
    Cool. Any surprises along the way? Anything you weren't expecting?

    Michael:
    18:44
    So, no, I wouldn't say there's anything that surprised me. I mean apart from the environment I had a much more perhaps rigid definition of, you know, you go to work and do your job and that's kind of that, but it's much more relaxed and that was, I guess, somewhat surprising.

    Erica:
    19:01
    Okay. I like that. Yeah. How bad the drive was maybe?

    Deepthi:
    19:06
    Yeah, I stay close by.

    Erica:
    19:09
    That's good. That's the way to do it. (Laughter) Yeah. What are you guys looking forward to for the remainder of your internships?

    Michael:
    19:17
    Yeah, so I'm looking forward since I'm rewriting these, these models and an interface for them, it'll be exciting to see them, how they perform and also to actually see the data and analytics team using them and hopefully finding them useful.

    Erica:
    19:31
    Yeah to see value for what you're working on. Absolutely.

    Deepthi:
    19:34
    So I'm about to finish the first part of my project, so I would like to wrap it up, you know, produce some good results and maybe get a publication out of it. And after that, yeah, I have a plan for what is to be done next, regarding the same, using the same similar approach but in a different setting. Yeah. So I'm looking forward to that.

    Erica:
    19:59
    Can you tell us what the different setting is or is that classified?

    Deepthi:
    20:03
    I'm not sure. (Laughter)

    Erica:
    20:05
    Right. We'll leave that one alone.

    Lingxiao:
    20:08
    So doing an internship here at TGS is an amazing adventure. I learn and discover new things everyday and I feel time passes very quickly, and everything is moving at a timely manner. So it's pretty good.

    Erica:
    20:24
    Nice. So I think we kind of touched upon how you guys are going to apply what you've learned here, at your careers as you go forward. Is there any particular job title that you guys think you're going to go towards?

    Deepthi:
    20:44
    Yeah. I probably will be going for a data scientist role, or I can say because of my background in reservoir engineering, I can go both on the data and science roles or the reservoir engineering roles. But yeah, from my experience here, I would, I think I would prefer to go to the data and data science roles because, there are like lots of opportunities out there and, the experience that I've gained here, I, I think it's going to be very helpful finding a full time position later on. Yeah.

    Lingxiao:
    21:18
    I could consider becoming a Geoscientist in the oil and gas or becoming a structural engineer because I have a programming background.

    Michael:
    21:32
    Yeah. I wouldn't say I have any career title I'm, I'm seeking out, but perhaps data scientist, but I'm not sure.

    Erica:
    21:41
    So what advice would you give to the interns who are going to be coming behind you?

    Michael:
    21:46
    Yeah. So probably to just build strong relationships with the team that you're in. Learn as much as you can, as deeply as you can.

    Deepthi:
    21:58
    Yeah. I would suggest that before coming in, you can go through, or if they have a set plan for you. In my case they did. So I had read up and you know, known what I'm going to work on so you can, you know, straight away start working on the project you have a rather than, you know, spend a lot of time, reading up those things that can happen before you start the internship. And yes, once you're here, it's, very important to like keep in touch, you know, meet the mentors every day or you know, update them so you have a clear path that you need to, yeah.

    Erica:
    22:44
    Lingxiao?

    Lingxiao:
    22:44
    I would suggest to go talk with people and you see what everyone is working on.

    Erica:
    22:51
    So learn, learn what other people are doing as well.

    Lingxiao:
    22:55
    Yeah.

    Erica:
    22:55
    That, yeah, that makes good sense. So why did you guys apply for the internships here?

    Michael:
    23:05
    So I applied, cause I was just looking for an internship and I had heard that, well I had heard that, (Laughter)

    Erica:
    23:14
    Honest.

    Michael:
    23:14
    (Laughter) I had heard good reviews from people who I respect and and I knew that they had a new data and analytics team doing machine learning, doing things with machine learning. That piqued my interest. And so I told them I was interested.

    Erica:
    23:28
    So kind of diverge off of that. So what programs are you guys using? Like actual hands on programs?

    Michael:
    23:36
    Yeah. So, programs for me are pretty, pretty simple. I use, a coding ID, visual Studio Code, and an Internet browser.

    Erica:
    23:43
    Whoa, okay.

    Michael:
    23:46
    I do that to do my work.

    Erica:
    23:47
    Google and a calculator, alright.

    Michael:
    23:49
    Yeah, pretty much.

    Erica:
    23:52
    Deepthi?

    Deepthi:
    23:52
    Uh, what was the question again?

    Erica:
    23:56
    What programs do you guys use?

    Deepthi:
    23:59
    Again, I guess we are in the process of making a program, so what I use is just Jupyter, it's very basic.

    Erica:
    23:59
    It's built on Python correct?

    Deepthi:
    23:59
    Yes, it is Python, I use Jupyter ID, and I'm in the process of making something useful from scratch.

    Erica:
    24:22
    So lastly, would you guys recommend a TGS internship to your fellow students?

    All:
    24:27
    Yes, definitely. Yes. Yes, yes. Yeah. Awesome. Yes.

    Erica:
    24:34
    Okay. So open question to the table. What are you going to take back to your program that you learned from your internship here? Starting with Michael to the left?

    Michael:
    24:42
    Yeah, so I'm learning a lot about machine learning and so in computer science that's obviously going to be a direct parallel. I can take that back. But I really think that what I'm learning most here that I'll take back is just how to collaborate with people, how to talk with people in a team and work in that way. I think that'll -

    Erica:
    25:05
    Life skills.

    Michael:
    25:11
    Yes.

    Erica:
    25:11
    Lingxiao?

    Lingxiao:
    25:11
    So, since machine learning in such a hot topic. Now, the work that I did here could be really extended into a project in my PhD research. So, yeah I'm currently working on that.

    Erica:
    25:28
    Awesome. Deepthi?

    Deepthi:
    25:29
    So right now we're working on a clustering of time series data. So my, one of the projects that I'm working, at my Grad school is also on time series data, and I think I might be able to, you know, use the insights that I gained from, from TGS, directly to my, research. So that's something that I'm looking forward to.

    Erica:
    25:52
    Awesome. Okay, well thank you guys for talking with us today and I guess we'll let you get back to work now.

    Michael:
    25:59
    Thank you for having us.

    Deepthi:
    26:00
    Thank you.

    Lingxiao:
    26:01
    Thank you.

    Erica:
    26:01
    And now our last group for this episode, the geoscience interns.

    Erica:
    26:08
    Going around the table clockwise, we have Sean Romito. You're from the University of Houston, majoring in geology. You are working towards your PhD and you are studying magnetic basement structure of the Caribbean plate, tectonostratigraphy of South Gabon and Camamu-Almada conjugate basins. I totally know what all of that means. What career are you working towards?

    Sean Romito:
    26:35
    Oh, hello. Thank you for having me. Definitely exploration Geoscientist, this is kind of where I've been propelling my career, ever since I started with a bachelor's and I've just kinda been stepping towards that goal.

    Erica:
    26:51
    Awesome. All right. Now we have Geoff Jackson from the University of Louisiana at Lafayette Majoring in petroleum geology. Your program is a master's degree and you graduated last spring. Congratulations!

    Geoff Jackson:
    27:07
    Thank you!

    Erica:
    27:07
    You studied a prospect lead off of a salt dome in southern Louisiana, and you cannot give us any more details than that.

    Geoff:
    27:14
    Unfortunately yes.

    Erica:
    27:14
    Very mysterious. So what, what are your career goals?

    Geoff:
    27:19
    Uh, similar to Sean's I was going to say, I can probably speak for the group here, but we're all just trying to be geologists and getting on with an operator, going to say probably best case scenario.

    Erica:
    27:28
    Awesome. Next we have Hualing Zhang, from the University of Houston, majoring in geology, working towards a PhD. And you're studying structural analysis and gravity modeling in the Permian Basin in West Texas. And you are originally from Urumqi, Northwest China and you got interested in geology about traveling around. That is so cool. So is your career goal the same?

    Hualing:
    27:53
    Yeah, basically similar, I'm working towards a career goal in the oil industry. Yeah. Since, like, my dad is also a geologist. Yeah. He works in PetroChina. So yeah, that's also my career goal.

    Erica:
    28:08
    Awesome. Yeah. Awesome. All right. And lastly, Cahill Kelleghan from Colorado School of Mines, majoring in geology. You're working towards a Masters of science and geology, and you're studying sedimentology and basin analysis / modeling with your thesis being in the Delaware Basin. So career goals?

    Cahill:
    28:28
    I'm pretty similar. I like to be in exploration geology and I really like sedimentology. So yeah, just applied geo science.

    Erica:
    28:36
    Awesome. Cool. So can you describe for us the projects that you guys are working on this summer? Same project or different project?

    Sean:
    28:46
    TGS has kind of tasked us with, I'm putting together some potential prospects or ideas of places we can look and most of that's going to be happening, well, we think it'd be North America and North American basins. And so we've kind of gotten access to some of their pretty amazing software, access to a lot of different databases and kind of putting that all together for a big picture of something useful that they can hopefully use from our projects. So I don't know if you guys want to add anything.

    Geoff:
    29:15
    Yeah, I mean, for one thing with these projects that's been very helpful to leverage the software that TGS has, specifically Longbow and access to their wealth of onshore well data that they have there. So we've been kind of bringing all of that together to generate these areas where we think that we should move further into as a company.

    Hualing:
    29:40
    Yeah. Also the first two weeks we're like working separately. We each have a study area and it's just a information gathering and doing researches and moving forward. Right now we are working in pairs. So, me and Geoff, we are working on similar location and to do like a research in a more detailed way. Yeah.

    Erica:
    30:05
    So you guys mentioned the software programs you're using. So aside from Longbow, what other programs do you use?

    Cahill:
    30:14
    Um, a lot, a lot of work in Kingdom. But Longbow yeah. Longbow and Kingdom. I'd say probably the big two. Yeah. yeah.

    Sean:
    30:25
    Any, I mean, any time you talk about geology, Arc Gis is going to come up. So we've definitely been using that a lot as well.

    Erica:
    30:32
    Okay. And is that different than what you were familiar with, from school or is this the same training that you had?

    Sean:
    30:39
    Well, Longbow is completely different. You know, even looking at production data is not something that I, you know, geoscientists when we ever, we go through academia, we even get exposed to. We use Kingdom. But I think it's, it's more of on a limited basis. I've, I've really been able to work a lot with, the, the well interpretation suites here at TGS that I hadn't worked with before.

    Erica:
    31:03
    Cool. How do you, do you find that challenging or kind of a natural extension of what you are already working with?

    Sean:
    31:11
    I mean, I, yeah, challenging, interesting, different. The team here, the geoscience team here has been very helpful, with the different, features. I'd say there are bugs. Some people might say they're features with the Kingdom software. (Laughter) but I'd say challenging. Yeah, but, but in a good way, not, not as a, you know, wringing out your hands kind of way.

    Erica:
    31:33
    So what else have you guys learned besides Longbow?

    Geoff:
    31:37
    I think for me is just kind of seeing just like what a day-in and day-out sort of process is like. So like having worked in the field, I never walked, I've never worked in a corporate environment before, but just kind of seeing how teams integrate and work together, it's going to say I've never seen that portion before. And so for me it's been fun, you know, going from classroom and then getting the actual hands on application of what we learned in the classroom. That's what's been fun for me so far.

    Erica:
    32:01
    Anyone else agree? Agree, disagree?

    Sean:
    32:03
    I agree. Yeah. No, I mean another thing that I feel a lot of us, especially me and with my Phd projects, they're very wide scale. I'm not talking about basins, I'm talking about plates. And so it's been very rewarding to kind of zoom in. Even if we are still basin scale, that's a lot smaller than I'm used to. So I'm able to kind of get lost in the details more than I would in a very large scale study.

    Hualing:
    32:28
    I think also a good thing is we learn from each other. Like where were you working together? Yeah, we're getting familiar with the software and if any of us found something and others will get around and see what we found. And I think that's very important for us to learn.

    Erica:
    32:48
    Yeah, absolutely.

    Cahill:
    32:50
    Yeah, I think kind of going off that as well and we obviously us for come from different backgrounds in Geo Science and what we've worked in and we kinda bring those backgrounds and each of our own projects and we kind of can come together and help each other out in different areas that we might not be more experienced with, like certain, well log interpretations or mapping things, stuff like that. So, so yeah, it's, it is helpful to have a team.

    Geoff:
    33:14
    Good overlap.

    Erica:
    33:14
    What's it like working at TGS, culture wise? The people, the food?

    Sean:
    33:22
    (Laughter) well they treat us well here

    Geoff:
    33:24
    I was gonna say no complaints there. Yeah, I mean getting started in know there's always a learning curve, but I mean I guess as much of a learning curve as there could be, you know, everyone around here has been as helpful as possibly could be, you know, to help make that climb that much less steep, if that's a good way of wording it. But that's kind of what I would think.

    Cahill: 33:43
    The food is definitely good. Healthy. I like it.

    Sean:
    33:45
    Can't complain about free lunches.

    Cahill:
    33:47
    Yeah. But, but I mean I think the culture here is really, everyone's been extremely nice and even just within the geoscience team, a lot of nice guys; Cian and Alex, they've been so helpful with any questions we have, whether it be geology related or software related, and we've had company outings already. Going on Top Golf is super fun. Everyone's very open to meeting different branches and whatnot. So that was really fun.

    Erica:
    34:12
    Why did you apply? Did it, for TGS' internship program in particular?

    Sean:
    34:17
    Well. Yeah. So, our professor, me and Hualing, we have the same, advisor at the University of Houston. Dr. Paul Mann. And he was actually the one that reached out to us because, James, the head of the Geoscience Department here, had reached out to him looking for good candidates. and he had asked us if we wanted to, to join up. We, we kind of, you know, we researched it. We, I was, I talked to James on the phone and it just seemed like something, so different from what I was doing at the moment that I felt like it was a great opportunity to jump back. And it, I have absolutely no regrets.

    Erica:
    34:54
    Awesome.

    Geoff:
    34:54
    Yeah, my story is pretty much the same thing. My thesis advisor was, was good friends with James K and so he reached out to me and saying, pretty much the same deal as him. Looked into you guys, obviously cause say Jason, I met you before. So that, and also, the interns from last year, I was going to say I was good friends with them too. So I knew what they did. And so, here I am.

    Erica:
    35:17
    Any surprises along the way? Anything that you weren't expecting that you've encountered during your time here?

    Cahill:
    35:25
    I guess one thing is, it shouldn't be surprising, but I'd always is that I'm working with really big data sets. There's always lots of errors you have to put up with. And even with the amazing technology we have, there's always, there's always a human aspect to it, that's always interesting, that we've dealt with in our data at least so far.

    Hualing:
    35:44
    I think for me it's the flexible working time and my, yeah, he didn't request a specific time to be here or like a specific time to leave. So that's like really helpful for my schedule that I can make adjustment along and try to see by what time range works best for me. Yeah.

    Geoff:
    36:08
    Yeah, that's definitely been nice. I feel, like you said having to commute from Spring. I was going to say, getting to come in maybe later or earlier as need be. It's always definitely nice to dodge that traffic.

    Erica:
    36:22
    What are you guys looking forward to working on for the remainder of your internship here?

    Geoff:
    36:27
    Well, I'm really excited to see the end product of what we're doing, especially because, we're going to be presenting it to upper management, and presenting it to our, our geoscience team as well. I think that's really going to help bringing it all together. Cause right now we know we're all working on our separate areas as well. I mean, we're still two teams in a certain area, but it's still very much our own work. And so that, that finish line I think is going to be where it all comes together and I see more bigger, I see a bigger picture than maybe I'm seeing right now.

    Geoff:
    36:57
    Yeah. I think one aspect that I like about is, it's not just busy work. You know, we're actually adding value to the company with an end result. Kind of like what Sean said.

    Erica: 37:06
    No making coffee?

    All:
    37:08
    (Laughter) Danggit. For ourselves, we make coffee for ourselves.

    Erica:
    37:14
    Um, what advice would you give to other students wanting to intern here?

    Cahill:
    37:20
    Say like, don't be afraid to get into anything that you're not experienced with. Whether it's geology or software related. Since coming here, I feel like you can learn a lot from a lot of different people and there's a lot of different backgrounds here and people are all open to helping you or talking about their passion and their little branch of geology or geoscience. And so I would say don't be afraid to ask questions and go up to random people and say, hey, what do you do here? And what are you into? Because chances are they're happy or passionate about their job and you can probably learn something from it.

    Geoff:
    37:54
    Yeah. Maybe to add onto those, don't feel like you have to know everything beforehand coming in. Cause I mean you're not, no one's gonna know everything. Kind of like what Cahill said, there's plenty of resources around. You don't feel afraid to ask. No. Everyone out here is more than willing to give their time to help you out for what you might have a problem with. And we've had that reiterated to us time and time again. So, I mean, it's been nice to know.

    Sean:
    38:17
    Hmm. And, I don't know if before we talked about how we got the internship, and I feel personal connections are the biggest, you know, it's not about going on a website and clicking apply. It's about going to the conferences and meeting people from TGS and they're extremely friendly. We've all seen that firsthand. So I'd definitely recommend, and I, I would recommend it as well that you would get an internship with TGS, but just go up and see them during conferences, talk to them, ask them about opportunities, say, Hey, what are you guys doing? Be interested. and even if you don't get something out of it, that's fine. You're still gonna make connection, connections and learn about where the industry's heading.

    Hualing:
    38:53
    Yeah, I definitely agree with Sean, cause I met Alex on with, the person, our geoscience group, we met during the AAPG meeting at San Antonio and I talked to him and, he talked to me about his project and what I may be expecting for my interns. I think that definitely helped. And yeah, when I first day, when I came here, I saw him as, hey, yeah, that's, yeah. I feel like familiar and yeah, I'm more easy to get along. Yeah.

    Erica:
    39:28
    What have you gained during your time here at TGS that you're gonna take with you as you continue your studies and your career?

    Sean:
    39:36
    Everything we just talked about. Yeah, no, I mean that, that's a good sum up question. So the, the connections we've made with all the people here, not just in the Geo science team, every, every other team that there has that there is at this company. All the skills that we're learning with these different programs, the different perspectives we're getting because we're looking at, again, not just geological data, we're looking at, these problems more holistically. All that and above, I think is what we're going to take with us.

    Cahill:
    40:02
    Yeah. I think, you pretty much nailed it on the head. It's seeing the, the geoscience in an actual industry application in its own way. It's a lot of different moving parts coming together for an end product that's ultimately valuable and generates business. And then seeing how that works, you know, if on a fundamental level that's, that's pretty interesting and being able to be a part of, it's pretty cool. So.

    Erica:
    40:27
    Well, awesome. Well, thank you guys for being here. Thank you for talking with us today, and we'll let you get back to work.

    Value Creation in Unconventional Plays Using Seismic

    Value Creation in Unconventional Plays Using Seismic

    In the second episode of Beneath the Subsurface we pick back up with a deep dive into onshore seismic technology in unconventional plays. Wayne Millice, Mike Perz, and Jason Kegel dig through seismic technologies, pre-stack seismic attributes, acquisition developments, and our predictions for the future of seismic and the unconventional realm. Erica Conedera, your host, new to the onshore seismic world, explores the challenges and sometimes over-hyped solutions with onshore acquisition and processing with our guests.

     

    TABLE OF CONTENTS
    0:00 - Intro
    1:51 - Onshore TGS History
    2:35 - Acquiring Onshore Data
    5:00 - The Migrated Stack
    7:28 - Resolution: The Bug Bear of Processing
    8:38 - Pre-stack Migration
    9:55 - Pre-stack Attributes; The Good and the Bad
    12:05 - Pre-stack: The Secret Sauce
    13:48 - Noise, Noise, Noise
    15:38 - The Future of Unconventionals; ARLAS, AI, and ML
    18:35 - Joint Study with FracGeo: Pre-stack Depth Migration
    20:39 - Analytic Ready LAS (ARLAS) and velocity Models
    24:33 - Acquisition Technology; Surface and Subsurface
    27:10 - Azimuthal Sampling - AVO and Velocity Inversion
    28:22 - The Q Problem (Anelastic Attenuation)
    30:08 - Frequency Problems
    35:21 - Interaction with Acquisition and Processing
    37:42 - The Future of Seismic in Unconventionals
    41:24 - Conclusion

    EXPLORE MORE FROM THE EPISODE:

    EPISODE TRANSCRIPT
    Erica Conedera: 00:12 Hello and welcome to Beneath the Subsurface a podcast that investigates the intersection of geoscience and technology. In our second episode, we'll deep dive into seismic technologies, pre-stack seismic attributes, acquisition developments, and our predictions for the future of seismic and the unconventional realm. From the software development department here at TGS. I'm Erica Conedera, your host and complete newcomer to the world of onshore seismic. I hope you'll find our discussion today as informative and enjoyable as I did.

    Erica:
    00:45
    Um, so let's start with introductions to my left.

    Jason Kegel
    00:49
    Yeah. My name is Jason Kegel. I've been with TGS for six years. I'm a geologist. I've worked on almost every one of the onshore US seismic programs that we have.

    Erica:
    00:59
    Awesome.

    Wayne Millice:
    01:00
    I'm Wayne Millice. I'm the gray beard of the group. I've been with TGS only about 11 years, but are, sorry, eight years. But I've been in the business about 35 years I'm the VP of onshore multiclient. And I'm here to hopefully teach some people about the value of seismic in our business.

    Mike Perz:
    01:19
    I'm Mike Perz. I am the director of technology and the onshore group. So I'm responsible for looking after all matters technical in support that group. And I'm not quite as gray bearded as the gentleman sitting to my right, but I have been in the industry for about 25 years. So I'm kind of blondish with whisps of gray, I guess you'd say. (Laughter) No spring chicken.

    Erica:
    01:42
    Awesome. So let's kick off the discussion for today. If you will Wayne by giving us a brief description of TGS' involvement in onshore.

    Wayne:
    01:51
    Sure. TGS was primarily an onshore-offshore company. Up until about 2011 and 2011, we started the onshore business, January I believe, if I remember correctly. And that's how long I've been here, since January, 2011. In 2012, we acquired a company called Arcis in Canada that gave us an instant library of about 15,000 square kilometers in the western Canadian sedimentary basin. And in 2012 we started our first project in the US. And, we have you a since grown the library from the initial 15,000 square kilometers or so until about a 34,000 square kilometer based our database based in the US and Canada. So it's been a, it's been a fun run and it's going well.

    Erica:
    02:35
    Awesome. So Mike, can you take it over for seismic technology? What do we do with the data once we get it?

    Mike:
    02:44
    Sure. So the first thing that happens is that data has to be processed and I always like to call a seismic processing the Rodney Dangerfield of the E&P chain. And the reason I say that is as you might predict, it gets very little respect, certainly in terms of the almighty buck and the price, the price point's

    Wayne:
    03:04
    Very little budget.

    Mike:
    03:05
    Yeah, very, very little budget. And it's kind of ironic because as Wayne and I have discussed a lot, it's the seismic processing step where we have maximal client engagement usually during the course of a multi client project and reputations are won and lost on the processing. But again, very little dollar value flows with it. I don't fully understand why the valuation isn't higher, but it's a problem that I certainly can't fix. So we kind of, in a way, we try to almost leverage that fact that it's a fairly, fairly cheap technology and we take it very seriously at TGS. So with that preamble about why it isn't the most highly valued element of the, of the chain, let's talk about some of the key outputs from processing. So the thing called the migrated stack is probably the single most important processed attribute in an unconventional play in say, offshore environments like the Gulf of Mexico seismic technology is no one buys CEO's of a big oil companies as an important de-risking tool for say sub salt plays the, in the case of unconventionals, I would not say that seismic has that same kind of universal traction whereby everybody in the c suites on down know about seismic. Nevertheless, it is gaining a lot of momentum.

    Erica:
    04:34
    And when you say unconventionals, can you elaborate on that?

    Mike:
    04:38
    Yeah, I'm talking actually we're all going to be restricting the scope of this discussion to the shale plays onshore shale plays. In a, well North America primarily, primarily

    Wayne:
    04:50
    Our primary focus on probably the Permian and the scoop and stack too. But there are several, several basins in the, in the US market that you could consider unconventional.

    Erica:
    04:58
    Okay.

    Mike:
    05:00
    Right? Yeah. So back to this business of the migrated stack, it is well accepted that it's a very useful thing in unconventional, development. And the primary reason for that is it helps in a delineating landing zones for the lateral wells and also geosteering and hazard avoidance. And I don't know, Jason, if you wanted to expand on a geological perspective of why those things are so important in the, in the depth domain. With seismic, you can start really understanding how to land your wells and doing geosteering in the unconventional world. That's one of the most important things that people are doing right now with their seismic.

    Jason:
    05:41
    Geosteering in particular and finding these landing zones has been important because these reservoirs are, we're looking for is the conventional reservoirs can be anywhere from 10 to 50 feet, which is a lot of times right around the [Clears throat]. The area of seismic resolution, what we found to be more difficult is sort of calibrating everything together. So when we have the data, so calibrating the well logs, the tops, some of the understanding the differences in the different tool parameters your measured while drilling tool parameters versus your after drilling parameters and how that relates back to a depth calibration has been very important in the seismic industry. bringing all those things together to geosteer real-time to actually find these landing zones has been something that a lot of different softwares have attempted to do. And bring this into a multi-client aspect where the operator can instantly get a depth to calibrate and volume that they can geosteer on or look at their regional area of interest onshore has been very different than offshore seismic, which has traditionally had that depth migrated volume to begin with.

    Wayne:
    06:53
    I can expand on one thing that Jason said too when we're talking about regional views on the petroleum systems. So our TGS has a strategy to date has been to get assets that are contiguous within these, with these within these basins so you can understand the regional view of it or of an oil producing basin or hydrocarbon producing basin. So it's important in our opinion that we get a large regional view. That's why you'll see you somewhere databases online. When you look at our, when you look at our projects, they're very contiguous and very focused on one area.

    Mike:
    07:28
    Yeah. Jason gave a nice description of of why we might want to use migrant stacks, for geosteering. And he touched on something important. You brought up resolution and you talked about thin beds on the order of 10 feet to 50 feet. And one of the real bug bears are an unfortunate reality in the seismic processing world is the fact that we really cannot dive down to smaller resolutions than, than those beds. In fact, we're probably operating in, in the order of like, wavelengths of hundreds of feet. So resolving those beds is pretty tricky. We can detect them sometimes but not resolve them and we're always being pushed on the processing side to do a better job. And it's disappointing because all, sometimes all the acquisition equipment in the world isn't gonna help you through that. Mother Nature is cruel in a way and she chews up the high frequencies and there really hasn't been a breakthrough in seismic processing technology to allow us to bash through that, that limitation. So resolution is an ongoing issue and we're always squeezed by it in the unconventional context in the, especially for this geosteering. So that's worth noting. And one other quick thing, Jason mentioned pre-stack depth migration and that's an important new technology in unconventionals. Technology has been around forever for 20-25 years in the Gulf of Mexico, but it's really gaining ground in unconventionals and in in fact, TGS, shameless plug for a talk. TGS is going to be hosting a talk in early June, June 6th. Mariana Roche Davies is going to talk about pre stack depth migration and why it's valuable in unconventional plays.

    Wayne:
    09:07
    We should be plugging a lots of things here, shouldn't we all sorts of-all sorts of shameless

    Mike:
    09:11
    shamelessly plug. (Laughter)

    Mike:
    09:13
    So, so if, if I could move away from the migrated stack, I just want to talk about the second big thing that seismic data is used for on the and the processing side. And that's the, the pre-stack data are used for generating attributes and we sometimes call this AVO analysis or Pre-stack and conversion. And the interesting thing here is that while the migrated stack has quite a lot of acceptance as a, as a really good de-risking tool for the reasons we mentioned, there is less universal acceptance o- the, these pre stack derived seismic attributes.

    Mike:
    09:55
    Some I can think of one really technically astute interpreter from a Permian player who's very successful and they don't touch the pre-stack attributes because there are too contaminated by noise. On the other hand, you go to the SEG or URTeC and all that, there's tons of talks on using these pre-stack attributes. So it depends on who you talk to. Some people use them, some people don't. My hope is that they're going to be used more and more down the road. We're kind of pinning a lot of our own technical direction on that, on that premise.

    Jason:
    10:22
    No pre-stack attributes have always sort of been the holy grail for, for people to find their, find their sweet spots. Right. I mean, looking at AVO in context, I mean that's the, the number one thing, right? And people are always absolutely to define their bright spot, right? And there's been tons of wells drilled just on that. But then to bring in rock mechanics and what they're doing with, with more pre-stack attributes in rock Brittleness and actually trying to look at Poisson's ratio and Young's modulus. When we start to look at those, we start to actually correlate the actual rock properties to what we're getting from our are sound frequencies. The more we can, we can do that and the better we can actually accomplish that is in the academic world has always been the, the, the driver. Right? And you can't talk to hardly any anybody that's teaching geophysics or rock mechanics or geology nowadays that doesn't want to talk about how to correlate your, your wells to your seismic. And it all comes down to understanding densities and shear wave and you're, you're compressed wave wireline tools and bringing that back to the, to the seismic world. unfortunately Mike is correct in saying that a lot of operators in these unconventional zones don't necessarily don't necessarily use it. They'll use it on their, on their own. They'll use a proprietorially, they'll use their own individual softwares to do that. But in a multi client aspect, it hasn't really caught as much traction as is, I think it will. And I think one of the big things that might push that is, regional is that, that's something you guys think the idea to have more regional studies of pre-stack attributes in pre stack, volumes.

    Mike:
    12:05
    Yeah, I think, I think that's a good idea. I mean what one of the nice things with our huge well database at TGS as we can, we can leverage that massive information source into these regional studies. And one thing I forgot to mention was that this pre-stack conversion or attribute business, it does very well to have a lot of well control and we've got lots of that here. So that would, that would certainly help garner interest. One of the big problems, I think that that detracts from acceptance is just that there are not kind of generic workflows for what to do with the pre-stack attributes. Once you, once you have them, it's quite easy to, stare down a migrated stack and figure out, I steer here, I land here.

    Mike:
    12:49
    That's it. You know that that protocol is easy to understand. What do you do with all these attributes? And different companies have their own secret sauce for that and sometimes they're quite tightly guarded about what they, what they do. So I think that may change in the future. We hope it does.

    Erica:
    13:02
    Why do you think it might change?

    Mike:
    13:04
    I just, I just think it will behoove everybody to leverage the seismic more everybody would win from, from that

    Erica:
    13:12
    To be more transparent with their methodologies or?

    Mike:
    13:16
    Possibly, I mean I think as as technologies emerge that-

    Wayne:
    13:19
    Or we push or we push the methodology, for instance, we have the data points internally that we need to start pushing those to those new solutions so to speak or so push them out and then our customers will create their own secret sauce from hopefully some of our solutions that we're aware of or a team.

    Mike:
    13:34
    And even as they push their secret sauce as the years tick away, typically people give up, they cough up their secret sauce to make a bad extended, a lousy metaphor. But they tend to divulge it and public domain and we all benefit from it.

    Wayne:
    13:46
    It's another paper at URTeC.

    Mike:
    13:48
    Exactly. So yeah, I guess this seismic technology thing is my bailiwick. That's why I'm doing a lot of the talking here that I was going to move on now to future future looking at data processing first of all and take a stab at what what I think are important technologies of the future. One is an old thing, it's noise, noise, noise, getting rid of noise, especially in places like the Permian. The Permian is so nasty as regards seismic soundings. You've got these horrible near surface layers, of anhydrites and salts interspersed and then you get these, these fills zones where the salt collapses and it, it kind of bedevil's all your seismic tools in many ways. And so that's why that one operator I was telling you about is reluctant to look at at their pre stack data for fear of the noise, screwing up their analysis. So we've got to do a better job at noise. We've got to do a better job at eliminating multiple energy. Full wave form inversion is a fairly well established technology offshore. We need to leverage that knowledge and get it going. Working better onshore for us, gets a nice velocity models among other things. Those are good for feeding this pre-stack depth migration technology.

    Erica:
    15:02
    What are the challenges of leveraging that?

    Mike:
    15:04
    Good question. The data are noisier on land typically. And so that isn't totally compatible with the full waveform inversion model to

    Erica:
    15:13
    So you have to adapt the model.

    Mike:
    15:14
    Adapted, got adapted to handle topography, things like that. And there are people are, people are doing that. We were certainly very active in that, in that space at TGS. Some of our competitors are as well. But again, I don't think there was this sort of routine commercial use at this point. I mean I know there's not just yet, but we're getting there. So yeah, those, those are kind of the big, the big things.

    Mike:
    15:36
    Now the last thing I was going to ramble on about a bit was taking a future look at interpretation. So where would interpretation be going for for unconventionals? Cause I mean, Jason, check me if I'm wrong, it's really a different beast than conventional plays where interpreters have, there there special ways to stare down data and pick sweet spots and bright spots. This is not that, that same thing. I and I could be off base here. I'm just prognosticating. I think that, one important thing in the future we'll be using machine learning and at TGS we could leverage our data and analytics group for this stuff and basically use machine learning to tease out complicated relationships between seismic attributes and production and completion data points with the view towards being able to predict from the attributes alone where the next landing zone should be the next well.

    Erica:
    16:32
    It's shameless plug. Our first episode was all about machine learning and AI. So please check it out if you haven't already.

    Wayne:
    16:38
    on there. So there're interesting conversations that our AI summit to sort of speak about who would be picking the next location. Would it be AI being confirmed by a human or human confirming AI. So there was a, that was pretty interesting discussion of that, that ti's a good point to bring up.

    Mike:
    16:57
    Yeah, for sure.

    Jason:
    16:57
    And when it comes to interpretation in particular with seismic and how machine learning can help having all of that data readily available in the cloud is, or the first step, right? So when it comes to machine learning, it's just a matter of the more data you have the in the, in the machine, the better you're going to have it coming out. But that's everything that TGS does have, right? The well data start including tops, production completion techniques, different attributes for seismic. Then you actually get the machines starting to actually tell you where your reservoirs are going to have sort of different permeabilities, right? If you could start understanding where these different permeabilities come in and these shales, very slight variations can lead to huge benefits in production. So that's a, that's a very big thing that we would love to be able to do, but it's not quite there yet.

    Mike:
    17:48
    Yeah, I mean I think you've raised a good point. We feel like we have all the ducks in a row here at TGS and it's, it's interesting because there- others before us have played around with multivariate analysis too to try to fit these attributes to things like production. They don't have the breadth of data that we have at TGS and they don't have as ready access to a lot of these things. So we're, we're poised to do some, some pretty cool stuff. So watch this space as they say. The only other thing I was going to say on on future looking interpretation wise, and I again I - disclaimers cause I could be wrong, but I believe that that combining seismic with geomechanical modeling software, may be an important thing to that end. And again, what is this our third shameless plug?

    Wayne:
    18:32
    Well we keep doing it because that's what we're here for. (Laughter)

    Mike:
    18:35
    So we're undertaking a joint study with FracGeo, a Geo mechanical modeling software and Services Company in the Permian Basin on our west Kermit Dataset in the Delaware. And we're going to be reporting back on that soon. But basically we're just, we're taking our seismic data and post-stack attributes like curvature to predict fault locations and that becomes feedstock for their Geo mechanical modeling stuff. And also the stuff you brought up, Jason Poisson's ratio and all the things we glean from inversions, those will go into their geomechanical modeling process as well. So that you know, hopefully that's a new sector in which seismic can be used.

    Erica:
    19:11
    We realized that we missed something, We need to circle back around to the topic related to pre-stack depth migration gentlemen.

    Mike:
    19:20
    Yeah. Pre-Stack definite migration in unconventionals. We kind of give it short shrift. I just wanted to add a few more more things. I had mentioned that it's a very established technology pre-stacked depth migration in offshore plays, Gulf of Mexico and such, and it's only been over the last couple of years that operators are using pre-stack depth migration a lot for unconventionals.

    Mike:
    19:40
    It's interesting to note you don't get the jaw dropping improvements on the migrated stacks that you do in the Gulf of Mexico because the data are not nearly as structured. Right, Jason?

    Jason:
    19:50
    Right, in most areas when people say railroad tracks, they're not kidding.

    Mike:
    19:54
    Yeah, yeah. So, so you don't get these amazing glossy brochure image improvements on the stocks, but the, the benefits come in subtler but still important ways. For example, you get natural output and in depth is one, one really important thing and another thing you get better fault definition after pre-stack depth migration. Sometimes I think the real prize can be the actual velocity model itself. One really important difference in velocity model building for pre-stack depth migration in the unconventional onshore case compared to offshore is that in the former case, in the onshore case, we've got so much more well data to constrain or lock down our velocity models, especially at TGS with our massive well database.

    Mike:
    20:39
    And so that's, that's a really, really good thing. So that's why I feel quite confident at the end of the day the velocity models are so responsibly constructed that you really can trust those depths and you get this natural depth conversion after depth migration that's as good or better than what an interpreter would do using his favorite or her favorite method for for depth converting time process data and on that well topic are TGS so-called ARLAS synthetic, well construction using machine learning. That's really gonna help our depth model building. We've yet to exploit it, but we're going to basically be able to get way more sonic wells through this ARLAS process to constrain interval velocities

    Jason:
    21:24
    And that's, that's a big benefit in the shallow, we start looking at the, the shallower area for drilling hazards and drilling risk. we also start looking at that for water, for water. So in the Delaware, it's a big issue, not only just produced water and injected water and saltwater disposal, but making sure that the, the drinking water in the aquifer water that's usually in the shallower intervals is safe. So it's an environmental concern that we look into having that velocity model better structured in the upper sections that we normally don't look too much into and we're looking at exploration per se onshore, helps quite a bit with that, both environmental and with, with hazard mitigation.

    Mike:
    22:05
    And the ARLAS construction will help that process, right?

    Jason:
    22:10
    Oh, absolutely. The ARLAS dataset- any type of velocity model that can improve on the, the prior velocity model is of big concern. So you can get back to geosteering. Anything that helps that velocity model. A lot of times when they are geosteering, they'll have realtime velocity model building as the mud loggers are providing new information. They cross different faults, they notice different things that can instantly update the velocity model they're using to help steer that well. So it just goes back to the fact that having the best velocity model up front is going to help the, the final piece of the puzzle, which is landing that well on the, the zone where you can get the most oil or gas out of it.

    Jason:
    22:53
    And that's been shown there. There's been a bunch of studies that have shown this, but there was one in the Balkan a few years ago that showed that using 3D depth seismic helped reduce their costs with 75% just by having their geosteerers use seismic. So that's you know, it's a known value for, for the, the seismic industry and the oil and gas industry to, to geosteer with depth migrated volumes. And it's nice to see that and the multiclient aspect that starting to really catch hold.

    Mike:
    23:26
    Absolutely. And let's just push it onto those pre-stack attributes.

    Jason:
    23:29
    No, I know, we just need it in the attributes.

    Erica:
    23:33
    Okay.

    Jason:
    23:34
    Particularly with faults. All right, so you're talking about some of the coherence studies with the post-stack, but when we can take some of that pre-stack ideas about Brittleness and Poisson's and Young's modelists and looking at those pre-stacks, bring it to the post-stack to where we can start identifying the fault structures and how those faults work. If you're interpreting those faults on your seismic before you go into your completion plan, then you have a much better idea of how you can track that well horizontally. So these wells nowadays, are a mile two miles long, some cases, I mean there, there they go for quite a ways going over some of these faults that have 20 feet to 50 feet to throw can greatly throw off where you're steering that well. So any type of better velocity model, will help you guide that. And a lot of times these faults, they're under seismic resolution. Again, so any type of fault or any kind of deviation that you can see in the seismic or with that velocity model is going to help you with your, your drilling plan and your completion plan.

    Erica:
    24:33
    Okay, so to pivot a little bit; acquisition technology?

    Wayne:
    24:36
    Well, I can chat a little bit about that. So I was in the contractor community for many, many years and back in the day we are pretty happy with, if you take it up from a spatial sampling standpoint, we were pretty happy at the end of the day when we were getting 100,000, 200,000 traces per square mile.

    Mike:
    24:56
    How long ago was it? How long have you been? 55

    Wayne:
    24:58
    Long time, yeah

    Mike:
    24:59
    when did you enter the industry? 65 years?

    Wayne:
    24:59
    At least 65 years. Yeah, (Laughter)

    Wayne:
    25:04
    I was still microfilming, right? (Laughter)

    Erica:
    25:04
    Sick burn

    Wayne:
    25:04
    I've been getting- yeah, I get that usually from him, so that's okay. But now, the contractor community has made significant investments in equipment and we're actually acquiring datasets that are, millions have millions of traces per square mile, not just 1 million, but millions of traces per square mile. Now they've been doing this quite a bit in the, Middle Eastern markets because of the terrain. The train's fairly simplistic over there. So the ability to put several thousand source points in one square mile or one square kilometer or whichever you choose to measure by Canadian or US, has- is quite simple. Whereas in the US, or the North American market per se, there is a lot more, what do we call, obstructions and they come from several people from several things. Mostly people I didn't slip there. That was a purposeful-

    Mike:
    25:58
    Freudian slip.

    Wayne:
    25:58
    Freudian slip yeah, But, so now that technology that high trace density wide azithmuth fully azimuthly sampled, that technology or that product is now available in the North American market. So, and it's getting more prevalent. We're starting to see a new acquisition techniques mostly with surface source because you're still limited in what you can do. Subsurface source, for instance, a dynamite, right. But with a vibroseis or any or other surface sources, you're able to acquire data probably for about the same amount of money. It was, like I said, I was getting 250,000 per square mile in 1996 and I'm getting millions for the same number today. Right. So it's a, they've seen significantly increased their their traits count, unfortunately haven't increased their profitability so that that's still a problem in the industry for the most part. But they're working on that. Hopefully at some point we can hopefully at some point we can, (Laughter) we can, get to a 10 million traces per squad or mildly because, go ahead.

    Mike:
    27:10
    I was going to say, you brought up the azimuthal sampling and that, that reminds me, I, I've been conspicuous by my silence on azimuthal AVO and velocity inversion techniques and these techniques are, are in use today using surface seismic to help characterize horizontal stress anisotropy and the presence of fractures and I kind of on purpose didn't get into it too much. I'm bringing it up now because I know that some, some of the, some of the listeners are probably wondering why we're not talking about it, that these things can be, can be useful and unconventional plays. But I'm avoiding too much mentioned because there's somewhat controversial and they have a, in my opinion, limited realm of applicability when they work, they work very well, but they have been oversold in over-hyped. So like I could, I felt I had to, I had to go there cause you brought up azimuthal. I'm going to turn you back to your, to your, your comments though.

    Wayne:
    28:01
    So as Mike, as Mike mentioned earlier, denser is better, but, as we've seen and we've tested and we've done all kinds of things in the field that mother nature has different ideas no matter how dense, we shoot these things. Once we drive that sound signal into of the ground, we don't know what's going to happen to it at the end of the day. So,

    Mike:
    28:22
    Yeah, for, for example, Q, I like to say Q can rear its ugly head Q mean is my proxy for anelastic attenuation. And I don't care how, how many sources and receivers you deploy, you can deploy them every, every fraction of an inch and you're not, you're not gonna change the fact that you lose your high temporal frequencies. And so that you know that that's a real problem. And then certain brands of noise are really well suited to being crushed or eradicated through dense spatial sampling. So that's wonderful. But some things like random noise, sorry, like, like really, really tricky linear noise. that's heavily aliased. If it's complicated enough, then you might need really, really fine sampling to deal with it. And that's still kind of a research topic. Random noises, easier, random noise. The denser, the denser it is, the more you'll, you'll beat down the random noise. No quibbles about it..

    Erica:
    29:12
    Maybe this is overly simplistic, but what causes Q, where does that come from?

    Mike:
    29:18
    Oh no, that's, that's a good question. It basically, every time the earth vibrates because a seismic wave is passing through it, the vibration has some loss to heat. And so it's not a pure elastic phenomenon. There's an energy bleed off and that, that basically that, that, that effect winds up, it's been, it's fairly, fairly straight forward and demonstrate that that kills the high frequencies of your seismic waves.

    Erica:
    29:45
    Okay.

    *Mike:
    29:46
    So yeah

    Erica:
    29:48
    If it's straight forward, then what-

    Wayne:
    29:50
    It's straight forward for Mike (Laughter)

    Mike:
    29:53
    It's straight forward from the viewpoint of the textbooks. I not going to derive that in real time, are you kidding me? No. My mind is mush over the years as I become more managerial and sales focused. So, but it's, it's well appreciated. It's well established in the community.

    Jason:
    30:08
    So how can new acquisition technologies help to mitigate some of those issues? Like are there other things on the horizon that there we're doing or you think that might, that might be out there to increase the frequency spectrum both low and high?

    Mike:
    30:20
    I, well maybe, let me return to the, the noise thing that first before I forget to reiterate, some of the spatial sampling might help to, to kill coherent noise that's alias. If you get a sample, fine enough to remove the alias. So that's, that's a good thing. But back back now to acquisition and the spectrum, the temporal frequency spectrum. Well on the high end with this Q effect or anelastic attenuation, honestly I don't think all the acquisition in the world is going to help you. If we need, we need to break through in other ways. Then there are some ideas about sparse spike deconvolution that had been around for a while. Maybe those will, those will improve over the years. On the low frequency side we are doing tangible things in the field. I don't know, Wayne, if you wanted to speak to them on the source and receiver side or,

    Wayne:
    31:11
    Sure. We're starting to do some, some experimenting, I think it's actually become more than experiment. We're actually acquiring projects with what we call either low frequency or low dwell sweeps, so we're starting in a real low frequencies and moving, moving slowly through the lower frequencies and then ramping up through the high frequency. So we're driving that spectrum a little bit wider so to speak. Right. So there's a lot of analytics going on on whether that works or not right now. Like you can comment from the processing side, but-

    Mike:
    31:40
    well it's interesting. Yeah.

    Wayne:
    31:42
    The equipment's there to do it as always. There's always been the equipment to do all this neat stuff, but stuff we create the data. Three C's a good example. We create three component data, but a lot of times we only use the p wave and not the transverse and the inver- and the, the, the, the three. So we don't use the three, we just use two and we create these volumes, but we got other stuff that sits on the shelf. But now we're starting to utilize some of these, low frequency start points, so to speak with a vibrators.

    Mike:
    32:09
    Yeah. Right. And same ditto on the receiver side, right?

    Wayne:
    32:11
    Yep. Yeah. Oh yeah. We're trying to, trying to go with the five hertz damp and phones instead of 10 hertz. We're trying all these things, but have we gotten there and put it into production mode yet? I think we're on the cusp.

    Mike:
    32:23
    Well, it's, it's, it's, it's interesting because a lot of clients are very interested in these technologies and there's definitely theoretical promise and we've demonstrated on synthetics that, you know, you can get good results by, by caring a lot about the low end. And we ran it a fascinating test that hopefully we're going to publish at an upcoming SEG workshop. Shameless plug number five, right?

    Wayne:
    32:42
    Four or five?

    Mike:
    32:44
    Five, six, I can't remember. So, so I'm a co organizer. Christof Stork is, is the chief organizer and along with Bruce Hootman and Rodney Johnston and myself work organizing this SEG workshop on land processing and acquisition. And we're gonna, we're gonna dive into some of these, some of these, some of these topics. And one of the things we're talking about is, are we actually really enjoying the benefits of this low frequency attention that we're, you know, that we're foisting on the soundings in the field. Are those low frequencies coming out at the end of the day after all our inversion products? And Are we really reaping the benefits? It's not clear. We ran an interesting internal tests where we, we acquired data with the low low hertz or low frequency phones and I think we had low dwell sweeps. We certainly had have lots of energy on the source side, on the low end and after preliminary processing the result, cause we had a control experiment where we didn't do all this low frequency attention and the preliminary processing showed that that when you were really attentive in the field to these low frequencies, you got a better answer. But guess what? After we got to final processing and we're able to use a second pass of something called deconvolution to really widen the spectrum, we found very little difference between the conventional acquisition mode and the and the the low frequency effort. This is at odds with some of the, some of the literature, and I'm not disputing other people's findings, but there might be a subtle effect with an area dependency to it. We'll see.

    Wayne:
    34:13
    But is a subtle effect enough to justify asking one of our contractors to go spend x number of dollars on equipment to upgrade their crews, right? Or it's,

    Mike:
    34:24
    I know it's a, it's a tough, it's a tough question. Tough question. You know, I guess if price points on the cruise side drop enough, sure it's Gravy, why not? But if not it might not be worth it. You might spend your money on other other things. I'm not sure.

    Jason:
    34:35
    Was it not the low frequencies that help you differentiate liquids in, in some of the inversions that you do further down the road? Is that the, that's the the biggest benefit, right?

    Mike:
    34:47
    That's I believe, I believe it's very helpful. The low frequencies certainly helped to, to lock down the low frequency model for the inversion they give you support. Where are you, at low frequencies, where you don't typically have such support with conventional surface seismic and, and I'm not an expert in inversion, but my understanding is some of the fluid effects do tend to show themselves better when you've got the right answer for the low frequency model. And that's facilitated by having some of these low frequency acquisition techniques in play.

    Jason:
    35:21
    You had mentioned earlier how the seismic technology and processing is the sort of the, the biggest area where we get interaction with our clients. Right. And it seems to be undervalued in that sense with acquisition. Is that a way we can of push that to, to fill that gap so we have that interaction and on both sides?

    Mike:
    35:44
    So interaction on the acquisition side?

    Jason:
    35:46
    Yeah.

    Mike:
    35:46
    Well it's a good question. I mean, my understanding is there's typically not a ton of engagement at the field acquisition stage yet. There's obviously some,

    Wayne:
    35:54
    Actually I would say yeah, there certainly is our one, our pre funders, write a check, they want to have some, implement some, some say so to speak what's going on. But mostly once we've made an agreement, on parameters, all that stuff is pretty much on us to deliver what we said we'd deliver. So, but we do where we really interact with our customers, we help them, we take problems off their plate so to speak, by taking on the acquisition piece, the acquisition piece is the most labor intensive, right. And, but where we really start to get in with our customers and when we, after we get the data, we've done the field acquisition, we interact with our customers from the processing side a lot. So it's important to us that like we said processing's a small piece of our AFE, but it's the most important because that's what we deliver, and that's what they see. Right. So, the, the nobody, no, I always say this to my guys to say nobody remembers the farmer that shot at you. Nobody remembers the vibrator they got stuck in the field, but they always remember if you're AVO volume was crap when they delivered it. Right. So they always remember that. But none of us other than other stuff that went on the field ever matters when they're looking at and looking at data on that workstation. Right? Yeah.

    Mike:
    37:07
    So this, the poor sister in the E&P chain is the processing somehow is, it seems to continually be this, this critical, critical engagement point for, for the client. I mean, I guess the client, they don't, they don't like having to deal with permitting and stuff.

    Wayne & Mike:
    37:29
    No, they only pay - like you guys - take the load off.

    Wayne:
    37:31
    We're taking that load off them. That's a big load. Trust me.

    Erica:
    37:34
    So jumping ahead, what do you predict for the future of seismic in the unconventional space?

    Mike:
    37:42
    Well, I think I state this without proof of course, but I believe that there's going to be an increased use of seismic, including outside-

    Wayne:
    37:51
    Well, the, the data that there's a lot of, there's a lot of data that's been acquired in the US and Canada for that matter. But a lot of it's getting dated, right? So when we're talking about, just like denser is better. We mentioned that earlier, right? Denser is better. So we're finding that a lot of these processing techniques that, Mike has been mentioning earlier, don't apply very well to older data data sets that don't have high resolution and aren't sampled very well. So we're finding, probably a lot of these older servers, you're going to get over it or getting acquired again, right? So that's, that's one marketplace. But as the unconventional space goes on, I think you're going to find, find it. A lot of these, like I said, a lot of these older datasets and a lot of the, are you going to make some discoveries within these data as the processing techniques get better and as we use the attributes better and all those things.

    Mike:
    38:42
    Yeah, 100% yeah. And I was going to say, I believe from my conviction that there'll be an increased use of seismic for that to reach for that to actually come into play. I think that we need to, as an industry use these pre-stack attributes that Wayne just mentioned more and more. And we also, I believe need to start using 3C converted wave data more. We didn't get into converted wave data at all on this Chit Chat.

    Wayne:
    39:06
    That's another, maybe that's another podcast.

    Mike:
    39:08
    It's - it could, in of its own, but you know, there, there's some great promise with that technology, like so many technologies, it's been oversold and over hyped to some degree. But there's some really interesting case studies in western Canada that show that it's got great potential. We had awesome converted wave soundings.

    Wayne:
    39:24
    Yeah.

    Mike:
    39:24
    On the loyal survey. Yeah. And that's so, so that might help to propel the increased use of seismic as well as increased use of these attributes. So that's, that's what I think is going to, it's going to happen.

    Jason:
    39:35
    One other thing, I really think that seismic is going to help in completion engineering. I'm going, I think that's sort of where it's going to now and where it's sort of, we've seen that happen with some of the pre-stack attributes and just to use seismic first off and understanding exactly where to perf and exactly where to make your completion intervals and where you're going to get the best production, on top of all the regional work you do to, to start out.

    Wayne:
    39:58
    And that'll impact the funding cost per barrel for our customers. So that's going to, we hope that that's the, again, the value of seismic, right? So how's that going to drive our business? How it's going to drive our customer's business at the end of the day.

    Mike:
    40:11
    Yeah, absolutely. And I mean one fundamental thing I forgot to mention, and Jason, you check me if I'm wrong, but I think what's happening in the unconventional spaces that there's a a slowly growing recognition that's actually probably accelerating right now. That to the tune that hey, we can't just go factory production style with completing all of our acreages there's enough geological heterogeneity that the production in this set of laterals here from this pad is kind of different than over here or even among the laterals in a pad. Why is this one so different? Parent Child Interactions, let's understand them better and all these burning questions, they're demanding some sort of better gaze into the subsurface and that is seismic.

    Jason:
    40:53
    That is seismic and that's where I think that's where you're absolutely right. That's where the future is driving it. If you can understand the parent child relationships between your multi well pads and pads next to you and how you're going to complete the entire basin on a stacked play basis, using seismic is going to be your, one of your only real tools to help out. And the better you have the air velocity models hammered down, the better you have your pre-stack attributes that can be involved in that study, the better off we are and I think we're well on our way.

    Erica:
    41:24
    Awesome. Well, thank you gentlemen for being here for our second episode. This was a really, educational discussion for me as someone who is not from a seismic background. And I'm sure I've heard listeners as well.

    Mike:
    41:37
    Been our pleasure, Erica. Yeah, yeah, yeah.

    Jason:
    41:39
    Thanks Erica.

    Wayne:
    41:40
    Yup. Good for-Thanks for dragging us all in here.

    Artificial Intelligence, Machine Learning and the Energy Industry

    Artificial Intelligence, Machine Learning and the Energy Industry

    In the inaugural episode of Beneath the Subsurface, we delve into the exciting realm of AI and Machine Learning as a blossoming new part of the energy industry. Arvind Sharma and Robert Gibson discuss and debate the impacts of disruptive technology, the importance of robust data libraries when building AI solutions, and the future of our industry with AI and ML solutions. With your host for the episode, Erica Conedera, we explore the factors that pushed our slow moving industry to this tipping point in technology and where it could be leading us. 

     

    TABLE OF CONTENTS:
    0:00 - Intro
    1:03 - Factors that brought AI to O&G
    5:32 - Job creation with AI
    12:05 - Career paths and team compositions in the industry
    15:30 - Industry pain point solutions with AI and ML
    21:32 - Clouds, open source and democratization
    24:24 - Kaggle and crowdsourcing Salt Net
    30:51 - Kaggle challenges with Well Data
    33:58 - Catching up with silicon valley
    36:49 - Approaching solutions with AI
    44:18 - Disciplining data and metadata to get to the "good stuff"

    EPISODE TRANSCRIPT

    Erica Conedera:
    00:00
    Hello and welcome to Beneath the Subsurface a podcast that investigates the intersection of geoscience and technology. And in our first episode, we'll be diving into the dynamic field of AI and machine learning as it relates to the oil and gas industry. We'll be discussing the impact of disruptive technology, the importance of robust data libraries when building AI solutions, and exciting possibilities for the future oil and gas. From the TGS software development team. My name is Erica Conedera. And with me today are Arvind Sharma, our VP of data and analytics, and Rob Gibson, our director of strategy, sales, data and analytics. Thank you gentlemen for being with us today for our first episode.

    Rob Gibson:
    00:48
    Glad to be here.

    Arvind Sharma:
    00:49
    Thank you Erica.

    Erica:
    00:51
    So let's start our discussion today by talking about the factors that brought the industry to AI and machine learning. Why now? Why not sooner? Why not later?

    Rob:
    01:03
    Well I'll start. Um, so thank you for the introduction, my name's Rob Gibson. I've been with TGS for almost 20 years now. And in that time, the thing that I have kind of seen over the 20 years in this company, , and probably another eight or nine in the industry, is that we've always been a little slow to adopt technology. And I come from the IT side of the world, software engineering, database design - so from my perspective, it's always been a little bit slow to bring in new technology.

    Rob:
    01:34
    And the things where I've seen the biggest change has been fundamental shifts in the industry, whether it's a crash in oil price, or, or some other kind of big disruptor in the industry as a whole, like the economy, not just our industry but the entire economy. But in middle of 2014 with the current downturn, that's really where I finally started to see the big shift toward AI, toward machine learning, towards IOT in particular.

    Rob:
    02:00
    But it seems like it took a big, big change in the industry where we lost hundreds of thousands of people across the industry and we really still needed a lot of work to get done. So technology has been able to kind of fill in the void. So, even as the downturn happened, we kind of started to level off at the bottom of the downturn and that's when companies started to see that we really needed to inject some more technology to get those decisions made. So generally speaking, I would say that this industry has been a little slow to move to adopt technology even though the industry has got a lot of money to invest in those kinds of things.

    Arvind:
    02:34
    Um, so thank you Erica for that question. And, I'm going to slightly disagree, more broadly, I agree with rob that um, oil and gas industry is historically a little slow in adopting technology, but, the reason I think is a slightly different, I think a oil and gas work in very difficult area where we need to have very robust proven up technologies to work. And in general, we wait a little bit for the technology to prove itself before adopting into, um, more difficult areas. So if we look at a little bit historical view, um, we have been on the leading edge of technology for a very long time. Um, some of the early semiconductors were built by your physical, um, companies. Um, then, as we moved to, PC revolution, we started actually PC, um, we started to actually pick up PCs into office very quickly, not as good as the silicon graphics people, but, soon afterwards, and then when the technology evolution started happening more in the silicon valley, then we started to regress a little bit. We continued on the part of what we were doing, whereas there was a divergence somewhere between mid nineties where silicon valley started to actually develop a little bit faster and we started to lag behind. And I think as Rob said, that, 2014 was a good time because at that time there was a need for us to adopt technology to increase our efficiency and, fill the gap that was created due to capital constraint. And as well as fleeing of, some of the knowledge base, employees - from our sector.

    Rob:
    04:39
    That's a good point on the technology side because you said that we kind of diverged away from where silicon valley really took off in the mid nineties. I entered into the industry in '94. So for me, my entire career has been that diverging process and just now it feels really good. Like we're finally catching up, not only catching up, but we've got customers, we've got employees who are sitting inside of the top tech companies in the world sitting at Google's facilities, even though they're an oil and gas company, sitting and working with Amazon, with Oracle, with IBM, with all these top names. And yet they're doing it in collaboration with the industry. Where in the past, it was almost like the two things were somewhat separated and now they are on a converging path. They've got the technology, we've got the data, at least in our space. And those two things coming together is kind of the critical mass we need to see some success.

    Erica:
    05:32
    So on that note, what kind of jobs do you think are going to be created in the future as the industries continue to convergence?

    Rob:
    05:40
    You know, that's a, that's a great prognostication. I mean, it's kind of interesting when you look back at like airbnb and Uber and those kinds of things. Nobody saw those coming and nobody knew what that was going to look like five years into their business, not to mention 10 or 15. I think that's what we're looking at in the oil and gas industry as well. We still have to find oil and gas. We still have to explore. We still have to be technologists, whether it's IT technology or G&G technology, we still have to operate in those spaces. But the roles may be very different. I'm hoping that a lot more of the busy legwork is a lot easier for us to work with and it has been historically, but we're still going to have to do those core G&G jobs. I just don't know what they're going to look like five years from now.

    Arvind:
    06:29
    I mean the way I see it is that it will be high-gradation to, like it will be more fulfilling jobs. The future jobs hopefully will be more fulfilling. So because a good portion of the grunt work, the work that everyone hated to do, but they had to do it to get to the final work, like final interesting work. Hopefully all those things will this machine learning and AI and broader digitization will help alleviate that part. And even whether you are technologist, whether you are a geologist, whether you're a geophysicist or whether you're a decision-maker. Like in all of those, um, you will start moving from the low value work to high value work. The technologist who was looking into log curve, they will actually start evaluating the log curve rather than just digitizing it. And that's, in my view, it's a more fulfilling job job compared to just doing the mundane work. And I, so that's the part first part is that what kind of job it, my hope is that it will be more fulfilling.

    Arvind:
    07:43
    Now the second is how many and what type of job, um, as Rob said that, the speed at which this is moving, we, it will be very difficult for us to do the prediction. Is that like if we sit here and say that they are, these are the type of job that will be created in five years, we'll be doing a disservice. We can actually make some guided prediction in which there will be need for geologist or geophysicist or petrophysicist and other people to do in what form will they be a pure geophysicist or a geophysicist who is a has a lot more broader expertise, a computer science and geophysicist working together. Those are the kinds of roles that will be needed in future because for a very long time we have operated in silos because it's not just technology is changing is the way we work is also changing is that we have operated in silos that we develop something, throw it over the fence. They, they catch it most of the time and then actually move into the next silo, and so on and so forth. Is that what-

    Rob:
    08:58
    You hope they do anyway.

    Arvind:
    08:59
    Yeah. I hope that they do anyway, but so that's the sequential process now. Some of them will be done by machines. Some of them will be done by human. And then you have to actually create a workflow which is like fulfilling as well as efficient for the capital investor.

    Erica:
    09:19
    Perhaps less siloed off?

    Arvind:
    09:21
    Less siloed off. So there will be team of teams and the team will actually move very frequently. So it will be almost like a self organization is that these are the four people needed to solve this problem. Let's take those four people and work on that problem. And then when that problem is solved or productionized, then they actually go solve the different problems.

    Arvind:
    09:43
    And so it will rather than back in the days or even today, hi- fully hierarchy of system, it will still be there, will be CEO (Laughter) and but there will be more, um, team of different group and different expertise, um, very quickly building and dismantling and those, that's the agile methodology that will be needed to take this technology and use it for, like basically doing things better.

    Erica:
    10:18
    So to kind of hone in on where you're saying, your background is in both geophysics and um, software engineering, correct?

    Arvind:
    10:26
    Okay. So sorry, I didn't actually talk about myself. (Laughter) So, um, I joined the TGS a little more than a year back, um, started as a chief geophysicist and then moved into this role. But before that, most of my career has been with BP and before that for a software company. So I have worked as a software engineer for some time and then got my PhD in geophysics and then worked for a little more than 10 years in BP all the way from writing imaging.

    Arvind:
    11:01
    So basically fundamental imaging, algorithm writing to drilling wells. So, in my short career I have seen a lot of things and what I do see is that, there has, there is a lot of silos in BP as well as in TGS. And BP is also working on it - breaking. I have a lot of friends there who are saying is that there is a significant effort in technology and modernization is happening in changing the culture rather than- it's not just about changing PC from going from a laptop to iPad. That's a- that's a tool. But the fundamental change will happen in the thought process. And if we want to actually use machine learning and these kinds of digital technology then it needs to be very integrated and the silo mentality is not going to work. You have to look at the problem as a holistic to solve it.

    Erica:
    12:02
    Yeah.

    Arvind:
    12:02
    So, so that's the background. So that's my background.

    Erica:
    12:05
    Yeah. So I asked because I wondered if you think that your career path is going to be the future of the industry, do you think that there are going to be more people with a dual background in both computer science and geophysics?

    Arvind:
    12:19
    So that's a very polite way to say that. My, I am actually looking at that my career is the right career. So, no and yes and no both. I do think that people will become more generalist and they will have deep expertise. And it's counter intuitive - is that generalist and deep expertise is not the same. Like we are used to someone who has a very deep expertise and that are not generalists about other topics

    Erica:
    12:57
    Narrow and deep.

    Arvind:
    12:57
    So very narrow expertise. But very deep and they have shallow expertise, very broad. Those are back in the days I think we are moving towards a deep expertise in several different narrow fields. So you need like, so to truly get good collaboration and innovation, you have to have deep expertise in several different fields to integrate them together.

    Erica:
    13:27
    So Rob, it looks like you're chomping at the bit here. (laughter)

    Arvind:
    13:30
    Deep and broad. So like what we need is deep and broad.

    Rob:
    13:34
    Yeah. When, when Arvind was talking about, kind of the career and, and some of the other topics, two things came to mind on the technology side of things. If you look back at AT&T, they had a choice and they did investigation and some pretty deep research on whether or not they needed to move into mobile cell phone technology. And they made the choice. They did a big expensive study and spent hundreds of millions of dollars or tens of millions of dollars to identify that they needed to be prepared for an industry of say, a million cell phone users by a certain year. And that number was, I don't know, 150 times wrong. It was way, way higher than that. And you could use the same thing with Kodak. They invented the digital camera and then lost the digital camera battle. And struggled in the industry. We want to make sure that we're looking broad enough to understand what's coming down the pipe and can adapt and change to that. Not just from the individual roles in the company, but the company direction as a whole.

    Arvind:
    14:34
    To give a concrete example is that , I have a background in geology or physics and computer science or Rob has background in geoscience and computer science and the data analytics team. It likes our TGS data analytics team. They have, we have people who have the um, physics backgrounds. They have PhD in physics and then they have worked in geophysics and then working on well logs. Then, the other one, Sathiya - he is a geophysicist who now is working on more of a deep learning problem. And a Sribarath is the team leader. He is a geophysicist. Who is it more of a computer scientist who is working on these two problems. So, our team composition itself, the TGS data analytics team composition itself is built in a multidisciplinary fashion.

    Erica
    15:30
    Yeah. So I'm glad that you brought up are our current team here cause I kind of wanted to pivot to the problems that we're using AI to solve for right now. You know, like what, what are the pain points in the industry and how are we using AI for that?

    Arvind:
    15:46
    So, so the pain point in the industry, are I'll talk about one, is it one which is very close to my heart. I was a, so in BP I did a lot of salt interpretation. So anything which requires a lot of human intervention is a big choke point because our data set is getting bigger, larger and larger with a lot more volumes to it are a lot more information to it and we have limited human resources and we want to actually take those human resources and mobilize them to do more high value work rather than doing a lot more um, grunt work. Salt model building is an example. And where we, we actually, our data analytics team started working there. So I'll, I'll work, I'll talk about that later. But that's an example where a lot of judgment call is made early, which don't require a lot of human judgment call early interpretation. Is the true place where automation and digital transformation can actually help.

    Erica:
    17:04
    Rob, what's your take on this?

    Rob:
    17:06
    Well, the Nice thing about what we're doing with salt picking is we're really helping us and our clients reduce the time it takes to get to the indecision. On my side of,of the house, my background with TGS has largely on the well data side of things. So it's not so much about reducing the amount of time of processing the data as it is getting a higher value data set in the hands of our clients. So historically, especially in the onshore U.S., there's a significant lack of data that's reported to the regulatory agencies. So we source that data as do a lot of other people. We source data from our, our, our customers, our partners operators. We process that data, but the most important thing that we can do with that is take that huge volume of data, the largest commercially available in the industry and add more to it so that the operators are able to get to that decision making process. So like Arvind said, if we can avoid the grunt work and get them to the point where they're actually making business decisions, that's what we're doing with our analytics ready LAS Dataset. We're in-filling the gaps in the curves because they either weren't run or weren't reported. We're predicting what the missing curves would look like, based on an immense volume of data. So it's not so much about getting the product created faster, although that is another goal that we've got. Of course, we're a commercial company. We're trying to get products to our customers and make money like anybody does. But the ultimate goal with our current analytics ready LAS product is to get the most complete dataset available so that the operators can make better decisions in the subsurface; drill less wells, drill more productive wells, drill wells faster. All of those things go into why we chose to go down that that path.

    Arvind:
    18:50
    So, looking at a higher level. The question that you asked was like what are the choke points and how we had actually using digital transformation in machine learning and AI to help that. Um, I think we published something like our CEO talked about that in the um, few months, a month back, Norwegian Energy day. There was a nice plot that, shows that most of the time we are acquiring data for a purpose. Like we are acquiring data to solve a geologic problem so that we can actually make a decision whether to drill somewhere, or not drill somewhere whether to buy acreage or not buy acreage by our clients. So when you take that data, you have to convert that into information, that information need to convert it into knowledge. And that knowledge is what enables our clients to make better, faster and cheaper decisions.

    Arvind:
    19:51
    And that cycle converting from data to knowledge to decision and enabling their decision is actually is the big choke point. If you want me to say one, this is that your point is that how to actually take data and convert to knowledge fastest way and cheapest way. And that's where most of our effort is. So salt, model building is an example where we right now it takes us somewhere between the nine months to a few years when we acquire data to provide the clients with the final image that they can do interpretation and make decision. This is too long of a time. In this day and age it needs to be compressed and a good portion of that compression can happen, by better compute. But some of them cannot happen without doing a deep learning where humans are involved in like for example, salt models building where like you can actually throw as much computer it as possible. But since the cycle time requires human to drill that model, it will be the limiting cases that, so there we want to actually enable the interpreters to take our salt net, which is our algorithm and accelerate the early part of it so that they have more time to do high quality work and build and build that model faster, reduce that cycle time so that our clients can make better, faster and cheaper decisions.

    Rob:
    21:32
    It's been interesting to watch the transition too with our industry and the technology at the same time we've moved to the cloud, right? All of our data's now sitting at a cloud provider and if you would have looked at the oil industry five years ago, there's a very security minded mindset around the industry that says, I need to keep that data because it's a very, very critical and I want to make sure the only, I've got access to it. So there was a lot of fear about putting data in the cloud several years ago. Now you look at the cloud providers and they're spending literally billions of dollars on things like security and bandwidth and access, things that didn't exist five, 10 years ago. So that transition to be able to go to the cloud, where all, where, all of our data sits today. More and more of our clients are going there as well. And the nice thing about that is you can ramp up your needs, on compute capacity, on disk capacity, on combining data sets across partners, vendors, other operators, and collaborate and work on that data set together to come up with solutions that you couldn't possibly have done before. So it's, it's fun actually to watch that transition happen.

    Arvind:
    22:43
    It is going a little tangent to the question that you asked her, but, because there's a very important point about the cloud services the the biggest cloud platform is Kubernetes by Google and that's actually open source. So Google developed that and made it open source available for anyone who wants to build a cloud infrastructure. They can have it. That's the, the most to use open source, platform that, available today. So that's changing the way people work. Like red hat or Linux, Unix, Sun, Sun, microsystem or Microsoft or apple. They are very, like, even in technology sector, they are very controlling of what they are providing to their consumers. They control that environment. Whereas now things are changing in which the open source systems like, which is publicly available is becoming one of the most dominant form of a software platform. Um, if you look at android for machine learning, it's tensorflow, Pi Torch. Those are open systems software that is a democratizing the technology so that anyone and everyone can, is able to take that next step and the solve complex problem because the base is available for them. They don't have to build the base. They can actually focus on solving the high value complex problem.

    Erica:
    24:24
    Speaking of both Google and open source and democratizing, problem solving. So TGS recently had a Kaggle challenge, correct, can you speak a little bit about that?

    Arvind:
    24:35
    So, yeah, that actually, so when I joined TGS, I had, one data scientist that we, we were working with, like we were still building the data science team and we started working on the salt net problem. We had an early, um, success. We were able to do some of those things and then we realized that there is like ocean of data scientists who are across the world. We don't have actually access to that Google actually open source and they have, they're working on their problem, they're working on Apple's problem, they're working on very interesting problems. So why they're not working on it at two different reason. One is that they don't have access to it in a second, the problem is not interesting enough for them. So Kaggle was our effort to make it accessible to everyone and make it interesting so that people will work on it.

    Arvind:
    25:30
    So just for the, um, description of Kaggle, Kaggle is the world's largest, data science crowdsourcing platforms. So crowdsourcing is a, um, where you put the problem and it's a platform or website where the, um, the problem description is given and data science scientists to work on their like on their spare time, nights and weekend or that's their hobby or that's their job. And they solved that problem. They submit to submit on that platform and they get instantaneous result that, how a good their solution was. So that's the Kaggle is the one of the largest world's largest platform for that recently acquired by Google. So we actually approached Kaggle that- can we actually put the one of the complex problem that we have on this website or this platform and they worked with us. And so we partnered together to host the oil and gas first serious problem for the automatically building salt model. And we actually, so to Rob's point, um, the hardest problem was getting the data rights that are convincing our management that it's okay to release a certain portion of data. We had to work really hard to create an interesting problem and that once we released that data, um, this competition was very successful in the sense that if they were around 80 plus thousand different solutions, just think of the scope of it

    Rob:
    27:06
    From almost 3000 different teams

    Arvind:
    27:09
    3,800. So close to 4,000 people. Oh yeah. 3000 team and comprise of almost 4,000 data scientists across the world work on this problem for three months and gave us more than 80,000 different solutions. We would have never got anything like this working day and night with whole TGS working on this problem.

    Rob:
    27:32
    I, I found it interesting because I like did a search on Google for our, TGS salt net.

    Arvind:
    27:39
    Yeah.

    Rob:
    27:40
    And if you look at the results just on Youtube, you'll find probably 20 different videos of PhD students, data scientists getting their master's degree who are using that problem that we posted out there as part of their thesis or as part of their Grad student work to show that, that the data science process that they went through as part of their education. And now that's out there for everybody to use.

    Erica:
    28:02
    So this is a major disruptor isn't it, to the industry because we have basically non geologists, non geophysicists solving problems for-

    Rob:
    28:12
    Yeah it's, it's definitely, we, there was a lot of teams, right? So there was some that had geoscience backgrounds, some that didn't, but most of them, they just come from a data science background, right? So they could have stats or math or computer science or anything. And when they applied this, it was interesting to see the collaboration on the Kaggle user interface where the teams were out there saying, hey, I tried this. What did you guys try? And the whole idea of crowdsourcing and, and the idea that we're kind of in somewhat of a unique position where we can do that. We can, we own the data. We don't license it from somebody else. Um, it's the data that we own that we can put out there. So we've got a huge volume that we can leverage and put it into a community like that where we can actually see some of those results come in.

    Erica:
    28:57
    So to kind of put you on the spot-

    Arvind:
    28:59
    Can I- one thing to say after that to is not just about data owning the data because there are several different companies who own data, even oil and gas company, they have their own data library. I honestly think that, it says volume about TGS, that TGS was willing to take a bet on this kind of futuristic idea and like go on a limb. But, and this is, I'm just giving credit to the senior management here, that they were, they're allowed us to actually go with this. That was one of the bigger hurdle than just to owning data, that management buy-in

    Rob:
    29:39
    Second only to data preparation for the challenge itself.

    Arvind:
    29:42
    Second only to the data preparation, it took us a lot of time to build-

    Rob:
    29:45
    Yeah

    Arvind:
    29:45
    an interesting problem. It's not just about like you have to create an interesting problem to-

    Erica:
    29:51
    to attract the right talent.

    Arvind:
    29:52
    So the winner was a group from a Belarus and the Japan. They have never met. They have never seen each other other than the Facebook.

    Erica:
    30:02
    Wow.

    Arvind:
    30:03
    And did they actually met on this Kaggle platform? They were working on this problem. They found out that there they are approaching with the two different ways and they actually teamed up so that they can combine this to create a better solution. Combining both of their effort and that that's actually happens to be the winning combination. But a traditional method won't allow us to tap into this kind of resources or brain power. That to someone from Belarus and Japan working together whom we don't know solving our problem and that is going to be a disruptor and we have to be ready to capitalize on it rather than be afraid of it.

    Erica:
    30:51
    Right. And that's why I wanted to go to rob, not to put you on the spot here, but as someone coming from the well data side, do you see any potential future Kaggle challenges using well data?

    Rob:
    31:05
    Yeah, the, that could absolutely be in our future. I think at this point we're really trying to frame the problems that we're trying to solve for our customers. And if we decide that one of those problems deserves, some time in the public, like on Kaggle, then we can absolutely go that direction. Not a problem whatsoever. At the moment though, our real focus is trying to figure out where can we provide the most value to the clients and we're kind of letting them steer us in a, you know, a way we have got our own geology department internally so we know what we need to do with our internal well data in order to high grade it to the next level product. However, we're really taking direction from our clients to make sure that we're moving in that direction. So yeah, I could see us having a problem like that, especially if it's starting to get into a Dataset that, , needs to be merged with another data set that maybe, we need support from, somewhere else in the industry. We're in a different industry.

    Arvind:
    31:59
    Just a few minutes on that is,the next problem I think that Kaggle need from oil and gas is a more on the solution side. So the knowledge to- like information to knowledge site in which you are all taking very different type of data set. For example, success failure database for the basin. And building a, prospect level decision that requires a, as Rob said, that collaboration, that the TGS collaborating with one of the E&P company or someone else, like those two or three companies and now bringing their data together because at the end of the day, this integration is what everyone is looking for. Can we actually create an interesting integration problem and put it on the Kaggle competition. So, any listener, if they're in, they have a good problem, they can actually contact Rob, or me. That, because we are always looking for good partners to solve complex problems. We can't solve all the problem by ourselves, neither other people. It does require teams to build the right kind of Dataset, interesting problems in to, to get into the board.

    Erica:
    33:22
    Okay. So we've talked about how we got here to this point in the industry with AI machine learning and we've talked about what we're doing today with the, um, let's move on to the future where we think AI will take, um, the industry. So to follow up on something that Arvind had said earlier, so you had said that we sort of fell behind silicon valley at some point. How, how far behind do you think we are right now in terms of years if you can make that estimation?

    Arvind:
    33:58
    Oh, that's a tough question but I'll try to answer it in a roundabout way. Is it that when I say that we lag behind, we lag behind in the compute side of it, like the AI side of it and some of the visualization and web-based technology when it comes to high performance computing, we were still leading up to very- probably in some of the spaces we are still leading. So storage and high performance compute which is both, oil and gas defense and Silicon Valley. All three are working. Um, we are not that far behind actually we might be at the cutting edge of it. And that was one of the reason that we didn't actually focus on the AI side because we were solving the problem in more high compute way and we are using bigger and bigger machine solving, more complex problems more physics based complex physics based solutions.

    Arvind:
    35:04
    So when it comes to solving physics based solution, we are still, at the front of the pack. But when it comes to solving a heuristic auto machine learning or AI based solution, we are behind, we are behind in robotics and things like that and we are catching up. So when you think of a mid midstream and downstream where there's a lot of the internet of things, IOT instruments, so things are getting is like instrumentized and there are a lot of instruments which are connected to each other and real time monitoring, predictive maintenance. Those are happening and happening at a very rapid rate. And that will actually, we'll, we'll catch up in a few years in, in midstream and downstream side or mostly instrumentation side where we are truly lagging is subsurface because it's not the problem that Ian, and like, silicon valley was trying to solve.

    Arvind:
    36:05
    A subsurface problem are complex. They are very different type of problem; that someplace you have very dense data, someplace We have very sparse data. How to actually integrate that and humans are very good at integrating different scale of information in a cohesive way, whereas that problem is not the problem that silicon like, technology sector was trying to solve. And so we are trying to actually take the solutions that they are building to solve different problem and integrating it or adapting that to solve our problem. So that's where like I see like, so I think it's a non answer but that's what the best I have. (Laughter)

    Erica:
    36:49
    It was a very good answer. So how does this change the way that we're building our products then our approach to getting our products out there?

    Rob:
    36:58
    Well, one of the, one of the things I'll start with is we're actually seeing our clients adopt analytics teams, analytics approaches, machine learning. there's a lot of, there's a lot of growth in that part of the industry. and they've gotten past the point where they don't believe that a predictive solution is the right solution. You know, with our ARLAS product, we're creating an analytics ready LAS dataset where we're predicting what the curves would look like, where there's currently gaps in the curve coverage. The initial problem the customers had was, do they believe that the data's accurate? We're starting to get past those kinds of problems. We're starting to get to the point where they believe in the solutions and now they're trying to make sure that they've got the right solutions to fit within their workflows in their organization. So I think the fact that they've actually invested in building up their own analytics teams where they've injected software engineering, geology and geophysics, a data science and kind of group them all together and carved them off, or they can focus on maybe solving 20% of the problems that they actually, attempt. That's kind of where the industry has gotten to, which means we now have an opportunity to help them get to those levels.

    Arvind:
    38:10
    You see that a change in conferences, and, meetings and symposiums that, like for example SEG Society of exploration geophysicists and, that, conference three years back there was one session about machine learning and last year, machine learning has the largest number of sessions in that conference. So you're looking at a rapid adaptation of a machine learning as a core technology in oil and gas and at least in subsurface, but most of them is at the very early phases, people are trying to solve the easier problem, the problem they can solve rather than the problem that need to be solved. So that's where there's a differentiation happening that everyone wants to work on machine learning and most of the people are actually taking solution to your problem rather than taking problem finding solution for a problem which is relevant. So,

    Rob:
    39:21
    I think that's pretty fair because,you've got to get some sort of belief internally and if you can prove that you've got kind of a before and after, here's what I did to make this decision or the wells that are drilled in the production I've got and here's what I predicted was going to happen. And you can start to see those two things align. Then you start to get belief in something. If you just use something that's predictive only and you've got nothing to compare it to, it may be the right solution. But do you have the belief that your company is going to run with it? So that's why I think we're starting to see them solve problems that we know can be solved initially rather than the big problem of say, if I shoot seismic here, I can predict how much oil I'm going to produce. That's a big problem and it's at different resolutions and scales than we believe we can solve and, and be definitive about it today. but I think that, I think I agree with you that they're, they're really focused on, on proving that this technology, that analytics that AI/ML is going to work for the problems that they know about.

    Arvind:
    40:24
    Agreed only up to a point is that, the reason and why I think it ML/AI solutions are different is because, in physics, one of our basic assumption is that, if we solve a toy problem, you can scale the same way is the same solution will apply on a bigger problem. That's not the case for machine learning solutions. The solution that is applicable for a toy problem is not going to scale. You need to actually retrain the data and the solution becomes different as the scale of the problem increases. So although it's, interesting to see that a lot of a small problem are very easy problem people are taking to- people are solving a lot of easy problem using machine learning. To show that machine learning works, that's good. But to truly take advantage of machine learning, you have to actually solve, try to solve one of the complex problem because you already have a solution for those easy problems.

    Arvind:
    41:40
    Why do we need machine learning? So for example, ARLAS is a good example. Our analytic ready LAS in which we are predicting well logs from the available, well logs. Now if I have only one well, or a few wells then I actually want my petrophysicist to go through the physics based modeling and solve that problem. I don't need AI to solve that problem. I have actually solutions which works there. If the solution that I need is that how to solve this problem on a scale of Permian basin or a scale of U.S. So like what we have done for ARLAS that the first basin we started was Permian is where we took all the data that we have as a training data or actually a good portion of that data as a training data set. We build that model, which is actually based in scale model that can actually ingest all the like 320,000 wells we have. So we used thousands and thousands of well as a training build a very robust model to actually solve that problem and now that solution is available for the whole basin. That's the kind of solutions that are problem that AI is good at solving and has actually best potential not for solving few wells. Learning about AI by solving a few wells is great, but as a product or as a true application of AI, we need to actually look at tackling the big problems.

    Rob:
    43:11
    Yeah, I agree. There's been a lot of, shall we say analytics companies that come out with a claim of being able to perform some sort of machine learning basis and they've got a great interface and everything looks really good. And the story behind it is that it's been taught on five wells or 10 wells in our learning set was in the tens of thousands of wells, which is why I believe in the data set that we've built.

    Arvind:
    43:40
    At a very high level, machine learning is like teaching a kid, like someone has come out of graduate school and they want to actually learn something and you are showing them this is how we actually do. The more things they see, the better they will get, the more experience they will have and the better their capability or work will be. So it requires the, the whole concept of machine learning or AI is that you want to actually train with massive amount of very high quality data set and that actually solves more complex problems.

    Erica:
    44:18
    How do you discipline data?

    Arvind:
    44:22
    So you are saying that did- have you talked to our lead data scientist and he calls him to himself a data janitor, that most of the time he spent is cleaning of the data and organizing the data so that he can actually do the high quality like the machine learning AI work. So if he spends his time like out of a hundred hours, 60 or 70 hours- so he's actually organizing, categorizing data set so that he can do the fun stuff in the last 30 40 hours. I mean that's actually, that's better than a good, most of the places where people spend 90 hours doing the curation and 10 hours doing the fun stuff. And that was one of the reasons why we had to build the data lake because one of the thing is that we need all the data to be readily available in a kind of semi usable format that I don't need to spend time learning about the 2003 data is different than 2015 data versus 2018 data.

    Arvind:
    45:34
    I need to actually consume it as one big dataset. So last whole year we spend actually considerable, considerable amount of time and effort in building our data lake in which we actually took all of our commercial legacy, data set and moved it on cloud. The two things that we did is one we standardized the data set so that lead data scientists don't have to spend on doing janitorial of data janitorial work and a second is creating metadata. So what Metadata is that aggregate information.

    Arvind:
    46:06
    For example, Arvind Sharma what is the Meta data about Arvind Sharma um, that he is five feet 10, I don't have a lot of hair. (Laughter) He drives some car and he, he has gone to- he has a PhD like so some aggregate information like out of her, like rather than cell by cell information about Arvind, what is the minimum, set of aggregate information that you can use to define Arvind. So that's the metadata about any data set. So what we did when we are moving this a massive amount of data set into our data lake for each of these data set, we extracted this aggregate information that where it was recorded, when it was recorded, what are the basic things done to this data set? What is the maximum amplitude in this volume? What is the minimum amplitude in this volume? What does the average amplitude in this? So those things we actually use it because a lot of analytics is that some of the higher level analytics will be about integrating the information about data set, like Facebook uses information about people to make some of the decision. We are not that creepy as that Facebook, but (laughter) it's, it's like taking the information about the data set and actually learning creating knowledge about the basin.

    Rob:
    47:37
    It's interesting when you were talking about the data janitorial work and how we've kind to standardize our data set on the, on the cloud because it kind of brings it full circle back to something you said early on. And that was that we want our customers to be able to get to that decision making point sooner without having to do all that data, janitorial work. I've been going to data management conferences for 25 years and I hear the same thing every year for 25 years. I spend "fill in the blank" percentage of my time, 60 70, 80% of my time looking for data and the remainder are actually working with it. That's what an analytics ready data set it's going to allow us and our customers to be able to do is not have to do all that janitorial work, but actually get to the point where I can actually start interpreting what that data means to me to make decisions.

    Erica:
    48:30
    So looking towards the future of the industry, do you think we're going to continue to ramp up in terms of speed and getting to the good stuff, the fun part? Do you think that's going to continue to logarithmically increase?

    Rob:
    48:44
    Probably faster than we can ever imagine. I think the, I think the change that we saw with companies moving to the cloud companies going toward, service based solutions, companies moving toward high volume, normalized consistent datasets, all of these things have been moving at light-light speed compared to what they were, the, the past 25 years. Up until today, every day about probably about every three weeks. We basically, have got some new technology that's been released that we can start adopting and putting into our workflows that wasn't there three weeks, three weeks prior, open source. It comes back to that topic as well. More and more of these tech firms are putting the data out as open source means we could leverage it and get to solutions faster. So to answer the question, absolutely faster than we can possibly imagine.

    Erica:
    49:28
    Well, awesome. I cannot wait to get to this future, with both of you.

    Erica:
    49:41
    Well, thank you so much for talking with us today. Being part of our first episode of Beneath the Subsurface, it was an absolute pleasure. If our listeners want to learn more about what TGS is doing with AI, you can visit TGS.com You can visit our new TGS.ai platform and, we'll have some additional show notes on our website, to go along with this episode.

    Arvind:
    50:06
    Thank you Erica.

    Rob:
    50:07
    Yeah, thanks a lot. I appreciate it.

    Conclusions and plugs:
    Check out the newly launched tgs.ai to dig deeper in to the data with subsurface intelligence. Gain detailed subsurface knowledge through robust analytics with our integrated data and machine learning solutions at tgs.ai Discover Geoscience AI solutions, Cloud Computing, Data Management, and our Data Library. Learn more about TGS at tgs.com

    Welcome to Beneath the Subsurface

    Welcome to Beneath the Subsurface

    David Hajovski and Erica Conedera welcome you to Beneath the Subsurface, a TGS original podcast. Our mission is to provide our listeners with insights into the current climate of the energy industry through expert voices talking about the biggest topics in E&P, geoscience, subsurface data and technology. If you enjoy listening to erudite experts nerd out and debate about the energy industry, you're in the right place!

    In this introductory episode David and Erica introduce themselves and the concepts of this show:

    • Our goal is to provide insight into the current state of the energy industry through expert voices talking about the most impactful topics that will affect our energy future.
    • Every episode will be formulated to touch base on specific and relevant E&P topics as well as invite our listeners to contribute ideas on future topics of discussion…topics that matter most to our listeners.

    We'd like to invite you all to listen, subscribe, and engage with our community at Beneath the Subsurface - where our community of listeners can talk freely about science, technology, and the people behind the industry that drive it's growth and development.

    Beneath the Subsurface is a TGS Original Podcast brought to you by TGS, the world's largest provider of subsurface data. At TGS, we provide global subsurface data products and services to the energy industry through our investments in multi-client data projects in an array of basins worldwide ranging all the way from new-entry frontier markets to established, mature basins. Our extensive library portfolio services the entire upstream life cycle from exploration through the appraisal and development stage.

    EPISODE TRANSCRIPTION

    David Hajovski: 00:01
    Hello listeners, welcome to Beneath the Subsurface, a TGS original podcast. I'm your host David Hajovski I'm a Vice President here in TGS and excited to be bringing you the introductory episode of our podcast series.

    Erica Conedera: 00:16
    And from the TGS Software Development department. I'm Erica Conedera, a QA engineer and fellow geoscience enthusiast. At TGS we provide global subsurface data products and services to the energy industry, through investments and multi-client data projects, and frontier, emerging, and mature markets worldwide. Our extensive libraries include seismic, magnetic and gravity data, multi beam, coring information, digital well logs, production data, interpretation, and advanced imaging services.

    David: 00:49
    TGS has joined the podcasting realm to present the industry with our unique perspective of the E&P market trends and demands as well as ask the questions that you, our listeners, want the answers to. Moving forward, our goal is to provide insight into the current state of the energy industry through expert voices talking about the most impactful topics that will effect, all of our energy future.

    Erica: 01:12
    Every episode will be formulated to touch base on specific and relevant E&P topics as well as invite our listeners to contribute ideas on future topics of discussion, topics that matter most to our listeners.

    David: 01:25
    So if you're in the energy industry, or just interested in listening to erudite experts, nerd out and debate about subsurface data acquisition and management, artificial intelligence, machine learning and its growing role in our world, new and exciting technology applications or even just the upcoming acreage offering and its implication for exploration. You're in the right place.

    Erica: 01:45
    On behalf of TGS and myself, I'd like to invite you all to listen, subscribe, and engage with us at Beneath the Subsurface where our community of listeners can talk freely about science, technology and the people behind the industry that drive its growth and development.

    David: 02:01
    Beneath the Subsurface is a seasonal monthly podcast. Each episode will come out on the first Tuesday of every month and our pilot season will comprise of six episodes running from May to October. Bringing unique insights from various energy experts. So with that,

    Erica: 02:19
    thanks for listening. Stay tuned,

    David: 02:21
    and let's enjoy the show.

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