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Episode 13: Digging Deeper with Michael Pyrcz


Digging Deeper with Michael Pyrcz, Associate Professor, The University of Texas at Austin, and 2019-2020 AAPG-AAPG Foundation Distinguished Lecturer.

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VERN STEFANIC: Hi, and welcome to AAPG's Energy Insights. I'm Vern Stefanic, and this is another podcast from AAPG in our ongoing series, Digging Deeper, featuring conversations with the speakers for this year's AAPG, AAPG Foundation's Distinguished Lecture series. Our guest today is Dr. Michael Pyrcz, an associate professor in the Cockrell School of Engineering at the University of Texas at Austin. Incidentally, Michael's DL lecture-- which is available for either downloading or streaming on the AAPG website-- is titled "Data Analytics and Machine Learning for Energy Geoscience and Engineering." Talk about timely, right?

And today, we get to know a little more about Michael-- his journey from rural Canada to the corporate world of energy to the position he holds today, which continues to be both connected to and also impacting and influencing the energy industry. And we'll talk about machine learning.

Michael, welcome to Digging Deeper. We're glad to have you with us today.

MICHAEL PYRCZ: Thank you very much for having me. It's a pleasure to be here with you.

VERN STEFANIC: Yeah. And so your talk, as I just mentioned, is about machine learning. And let's get right into it, because you have what we've found to be a pretty cool life-- a pretty cool journey in your life. Where you started out, how you went through the industry, and now at an academic institution, but still talking to industry. And a lot of what you talk about, though, deals with machine learning, which you have sort of been part of for many years. But it wasn't really called machine learning, is that right?

MICHAEL PYRCZ: So, clearly, it's a case of we're all learning together right now. There's a wave of the technology going through. But the way I see it is machine learning is statistical learning. And we have been working with big data. We've been working with statistical models of the subsurface, combining multiple sources of information, working with uncertainty, and putting that all together for the purpose of inference and prediction the whole time. And so, for me, it's very natural for us to make the extension into what would be more properly known as machine learning nowadays.


MICHAEL PYRCZ: So, yeah, I would say, I think I've been working in that area for a while. And I think many of us have been working in data analytics and statistical learning.

VERN STEFANIC: So how come everybody's scared to death? Not everybody, but a lot of people. You hear about-- there's an intimidation factor about the concept of machine learning. What's going on?

MICHAEL PYRCZ: So I think there's a lot of misunderstanding about the technology. Now, I think it can be-- it's interesting, there's a whole continuum. It can either be quite incremental-- in the form of automation of some of our common tasks-- and in some cases, it may be disruptive or transformative. I like to say transformative and be more positive about it. But no matter how I see it, I think there's great opportunities to see new value, to be able to make our jobs more focused. Geoscientists will spend more time doing geoscience, that's how I see it. We'll spend less time clicking in our interpretations. We'll have more guidance.

I think sometimes our interpretations are very challenged by all of the different types of data that we try to integrate. And what we'll find is that these new tools will help us avoid contradictions. I've seen that before, working on subsurface asset teams, where I don't think everybody quite agrees around the table. And then it's not until you build the model that you see there's contradictions in the concepts, right?

VERN STEFANIC: Yeah, yeah, yeah, yeah. So in talking and giving all of us an idea of the scope or the magnitude of what's going on with machine learning, is this something that industry-wide everybody's trying to pick up on right now? Or is it just a couple people are saying, we're ahead of the curve?

MICHAEL PYRCZ: Definitely different companies are at different positions as far as readiness in this digital revolution, that's for sure. But just last year alone, I think I visited and taught within about 20 different companies. I do still teach my courses, in case the Chair is watching this video, right? Now, but I'll tell you what-- every single company I go to, they're facing the challenge and they're trying to do something right now around digital technologies, data analytics, and machine learning. And I've seen a whole range of responses. I've seen a lot of companies who are-- we have a couple of people, maybe they've hired a couple data scientists, we're trying to fast follow or trying to understand what's going on. I've seen other companies that say, we want to be at the lead.

There's one company-- Conoco Phillips-- they have a huge effort to teach hundreds of engineers and geoscientists to make them citizen data scientists, which amazing effort, amazing program. And I've seen a whole range in between there of different efforts around this. I have seen, as far as adding value, a lot more focus on the data. And to be honest, you know we have P10 and P90?


MICHAEL PYRCZ: To me, the P10, or the low side outcome-- a Chevron P10-- would be, we just do better with data. We do better with understanding statistics. And more people understand how to do basic coding and scripting so they can automate and be more efficient in what they do. I think that-- to me, that might be where we see much of our value.

VERN STEFANIC: So what is the state of the art of data, right now, that the industry is using?

MICHAEL PYRCZ: So it's a very good question. When I go from company to company, I see similar types of challenges. Data storage and curation is a huge challenge. What I'm seeing is a lot of data sets for which there is a combinatorial of versions because of multiple interpretations, uncertainty, data that needs to be integrated into the-- or concepts have to be integrated together. And the result is, often this is not stored in such a way that, as we move through project life, which often has, as you know, cycles of many professionals working on the same project, right? People don't stay on the same project. Things can fall through the cracks.


MICHAEL PYRCZ: And we may not fully harness what was done previously. I see there's many issues around data like that. I think when it comes to metadata, we also do struggle as an industry. How do we track-- how do we keep track of all of the assumptions, all of the choices that went into formulating that data? Many of the data we work with isn't really primary data, right? It's not directly from the measure, it required layers of interpretation on top.

So I think-- to be honest, I think, about a decade ago, I saw a lot of companies go through a revolution, saying, we're going to ramp up and have this great universal database. And they start to look at database technologies, which in itself is its own scientific field. And they were looking at, how do we have standards, how do we put it together, how do we get everything to talk to each other? I don't think we succeeded there. I don't think that finished for most companies. And so I think we're still going to face challenges with data as we go into data analytics.

VERN STEFANIC: So most of the challenges now-- we may not even know what the big challenges in interpretation are going to be, because we still don't have-- it's garbage in, garbage out, so to speak.

MICHAEL PYRCZ: Yeah. And so this is part of the reason I'm motivated to teach. So I've worked to actually completely change the curriculum for our undergrad program at University of Texas at Austin. I'm doing that in my department, I'm also helping over in the College of Natural Sciences with the freshman research initiative. And I want scientists and engineers to understand statistics, programming, data analytics, and machine learning, so that as they go out there, they're able to pose the questions.

I had a student come in my office and they said, I've got this great idea-- I'm going to take seismic information, well information, I'm going to do these great forecasts. And I said to them, I said, what are your predictor features? And they didn't know. They hadn't broken it down to the nuts and bolts. They didn't understand well enough to be able to pose the question in a manner that could be used by data analytics and machine learning.

VERN STEFANIC: Actually, I want to come back to some more of your experiences in the classroom and teaching. But getting there, the one thing that is obvious in your lecture, and is apparent even from us talking here right now, is that you have a passion for learning about things, seeing challenges, and figuring out how to go forward through them, and trying to take steps forward, always. It seems to me like everything is advancing, advancing. Could you talk a little bit about your background? How did all that start? For some of us who don't know you that well, where are you from? What's going on?

MICHAEL PYRCZ: So I think I can best describe myself as a farm kid from Alberta. I came from a small town. I didn't grow up on a farm but I was working on a farm. I spent a while during high school working as a waiter at a local restaurant. And then went into a dairy farm and realized I work with cows better than customers in the restaurant. I really enjoyed the cows and I really enjoyed the manual labor and all of that aspect of it.

OK. So I came out of that. And from a family that actually was, I guess, we could characterize as low income, not a lot of opportunities. I'm first generation to-- I'm the first to get a university degree from the family. So it was kind of a big step. I went out and just did that. I was driven to do that. And what's really funny is I hadn't really thought about university, I had no concept of it. And it was just a random conversation with a university student that led to the whole thing. I was in grade 11, I was working full-time, had a girlfriend, had the car payments, had all that stuff, and I was doing terrible in high school. I wasn't very good at all, my marks were terrible.

VERN STEFANIC: You were not a good student.

MICHAEL PYRCZ: I was not a great student at that point. So grade 11-- fortunately, this was Canada. I'll get back to that. So I'm filling up my old, rusty car, and another person pulls up at the pump on the other side on a cold evening-- dark and cold evening there in Alberta. And he starts talking to me. And this is a Canadian thing, we talk to strangers. We totally do. And so he looks at me and he says, do you know how that engine works? And I said, what do you mean? I knew something about the internal combustion engine, four cycle, whatever it is-- four stroke. And he said, no. He started to explain the theoretical carnot cycle, about entropy, and pressure, and temperature. And they were an engineering student working materials engineering, and talking about how they were building--

VERN STEFANIC: You didn't know this person?

MICHAEL PYRCZ: Didn't know the person. And they drew the chart, the entropy plot, directly on a windshield with frost. They were telling me these things. And it was amazing. I was just taken aback. I'd never really known engineers or geoscientists or scientists, and they were explaining these scientific concepts. And I looked at that and I said-- that night, I said, I'm going to be a scientist. I'm going to be an engineer. I'm going to learn this stuff.

So the next day, I went to high school. I made an appointment, met with the student counselor, Mr. Jamison. I've thanked him later on, don't worry. And Mr. Jamison, he looked up my marks, he looked at me, and he said, Michael, university is not for everybody. University's not for everybody. My marks were not even close. And from that moment, I cut down the hours at the restaurant, I focused, and that was it. That was my mission, to go into university.

Well, I got accepted in engineering. Part of the drive for that was the engineering program-- they had a mining engineering program that was very geologic, but it also had a co-op program, so I could work half the time. The geosciences didn't have that. So for my undergrad, unfortunately, I was forced to do mining engineering instead of geoscience, which is what I really wanted to do. And I graduate number one in my class. I was on fire, I was driven. I was just like, this is it, this is my life now. And so I went all the way through to PhD, which was much more geologic, subsurface, modeling, and geostatistics.

VERN STEFANIC: Where was the education? Where did you go to school?

MICHAEL PYRCZ: Both degrees, University of Alberta.


MICHAEL PYRCZ: Up in Edmonton, Alberta. So that explains things-- the cold, frosty evenings in the winter. I think you could understand how bad it could be. Minus 40 is not atypical.

VERN STEFANIC: Wait, did-- probably not, but I got to ask. So the person who you had the conversation with-- did you ever-- who was it? Was it-- did you ever find them?

MICHAEL PYRCZ: So I know that they were a student-- kind of a senior student-- from the University of Alberta in materials engineering. But I never found them. I thanked my guidance counselor. Because Mr. Jamison, he made me mad.


MICHAEL PYRCZ: He made me so mad that day. I said, what do you mean, it's not for me?


MICHAEL PYRCZ: I can do it. And it motivated me. But, no, I'd love to meet that student again. But I'll tell you what-- now I do the same thing. To me, the whole world is a gas station. I'm serious. I walked out of campus, as I often do, like 8:00 at night because I'm so busy. And I'd forgotten to eat all day, because I'm just way too busy as a professor. And I walk out and there's a student just standing at the back door in the dark there, propping the door open. And I thought, well, as a professor-- I thought it was a student-- I said, as a professor, I should ask, what are you doing here, kind of thing. Is there a security issue or something, the door being held open at night and all, right? And they said, well, there was some type of event inside they were attending. It was a Lan party, actually. A bunch of the engineering students that got together and playing Lan games, or whatever.

And then I said-- so I looked at them and said, are you a student here? And they said, no. And then I looked back and I introduced myself, I'm Professor Pyrcz. You're very much welcome here, nice to see you. They introduced themselves very politely. And then I said, you should hang around here a little bit more often, we'll make you into a scientist or an engineer. And I just thought what that meant. Like if it had been me in their shoes, for a professor on campus to take the time to welcome me and to speak to me like that--


MICHAEL PYRCZ: Would have potentially changed my life. I thought that way. And so every time I meet with people now-- I'm funny. I go to the HEB store, I tell people I'm a professor, because it always ends up in a conversation. If a bunch of students are working and, OK, what are you going, where are you going next?

VERN STEFANIC: Yeah. So there's a part of you not only that wants to learn about more, but you also have the other side wanting to give back.

MICHAEL PYRCZ: You want to share it, yeah.

VERN STEFANIC: Does that come from your family? Does that come from the Boy Scouts? What was it?

MICHAEL PYRCZ: So it's a really good question. I grew-- I think my situation growing up was pretty difficult and very challenged. And I think, because of those struggles, I have a high degree of sympathy. I can put myself in other people's shoes, I can imagine. And so I think that partly motivates me. I think the professor I studied with during my PhD, Clayton Deutsch, he had developed open source software for geostatistics that was used all over the world. It was used to develop all kinds of software packages that made-- I'm sure-- fortunes, and he didn't worry about it. He just freely gave things away. And what I learned from him was when you do that, it does come back.

And for me, what am I concerned about? I'm concerned about finding funding for students, helping my students do well. And so what I've done now is I give every one of my lectures away. Now, part of it is also just practical. In this modern digital age, if you record your lectures and give it to students, guess what? They're going to give it away. Do you know what I mean? There's internet sites where everything can-- my book that I wrote with Oxford, you can get it on some site right now, the PDF.


MICHAEL PYRCZ: So what I realized was, just give it away myself. And so I record every lecture, I record a product that's useful for professionals, useful for my students. People appreciate it.


MICHAEL PYRCZ: And I get to be part of something, I get to do something great. And the result is-- I'll tell you what-- I go into meetings with companies and I'm like, shall we talk about what I do and then we can talk about maybe collaboration? And they said, don't worry, we follow you on Twitter, we watch your lectures on YouTube, we know what you do. Let's talk about collaboration, let's talk about funding students. That's a dream as a professor. To me, that works perfectly.

VERN STEFANIC: Way cool, way cool. If you could fill in a blank or two before we-- we keep wanting to-- I keep wanting to get to the education and some of the ways that you interact with students today and everything. But even before that, to fill in a couple of blanks. Because you were in industry for a while. So you get your PhD.


VERN STEFANIC: And what happens career-wise after that?

MICHAEL PYRCZ: Straight to Chevron.

VERN STEFANIC: Straight to Chevron.

MICHAEL PYRCZ: Working in a technology company, yes.

VERN STEFANIC: OK, OK. You're doing what?

MICHAEL PYRCZ: So starting out, it was more practitioner, working on service projects, I'm getting some experience doing subsurface modeling in the industry. During my PhD, I had supported myself and my PhD by consulting at the same time. So I'd done a bunch of consulting for a bunch of energy companies out of Calgary. And so, when I was in Chevron, I got--

VERN STEFANIC: You say that casually. That's pretty impressive.

MICHAEL PYRCZ: It was good. It was good. I joke, I took a pay cut when I start working with Chevron, out of my PhD. I had two kids while I was in my PhD. And my wife was home with the kids, and I was providing for a family while doing a PhD. It was great, it worked. And I was fortunate, I had great opportunities.

But when I started with Chevron, I had an opportunity to learn the Chevron way, the Chevron methods, to just drown in amazing data sets. And to be mentored-- you know, you get a PhD but, come on, you've got to realize-- the people in the industry have been doing it for 30 years, sit down and just learn what they know. You know what I mean? So people like Scott Meadow, Sebastian Strebelle, who was my team leader, Morgan Sullivan-- amazing mentors. I got to work with Henry Posamentier for a while.


MICHAEL PYRCZ: Just great people. Mitch Harris. I just love these people.

VERN STEFANIC: Great names, great names.

MICHAEL PYRCZ: It was a very wonderful-- it was a wonderful time to be there. And so I got to learn a lot, grow up in that. And then I found that I really liked research. And so I start to develop, I start to code, I start to learn more quantitative methods, the data analytics, more of that. And it reached a point where I start to lead the team, lead the projects, lead the program. It was several million dollars worth of research a year that I was running.


MICHAEL PYRCZ: And then, right around that, the opportunity to go back to academia appeared. I'd written a book, I'd had about 40-something peer-reviewed publications while still at Chevron. And academia came knocking.

VERN STEFANIC: By the way, Henry and Mitch Harris, oh, wow. Great, great, people.

MICHAEL PYRCZ: I love them.

VERN STEFANIC: Industry-wise, profession-wise, AAPG-wise. But through this-- before we leave-- since you were doing research, I mean, that that would be perfect for the way you are in wanting to keep learning more and learning more and learning more. Was this in an era-- and I know we've talked before that machine learning has always been with us, we just call it different things-- but is this where some of those real ties and connections started for you?

MICHAEL PYRCZ: I think-- thinking about the world and our data sets and what were the new lenses we could use to try to explore and understand that. It was a general mission of, we can quantify everything. It's kind of been my attitude the whole time. And if you think about it, that's the prerequisite for everything machine learning. You have to feed in numbers, you can't just feed in some subjective interpretation. You have to quantify. And so I think that really expanded my interest around data analytics.

And then, of course, we got into basic machine learning methodologies-- all over the place-- but we call it more statistical learning. Machine learning did take off just in the last couple years. I know this is the fourth wave of it, but as far as taking off again. And so now we've-- now, at the same time, when I teach my machine learning course, I say something. And I say, if you take my data analytics, my geostatistics course, I can stand up here and say, I wrote the book in it. I know it, I've published lots on it, I'm an expert, recognized expert. What I say in machine learning is I speak from humility. And I say, guess what, we're all learning together a little bit. Because the field is moving so fast. There's so many things. But I'll tell you what, understanding fundamental statistics and data analytics, you can understand in depth what's going on with machine learning, generally.

VERN STEFANIC: OK. Was there a point-- I want to say this politely and without sounding snarky-- when you realized corporate culture wasn't for you? Or was that part of the realization that you wanted to give back?

MICHAEL PYRCZ: So I'm kind of-- I'm unusual. Because I got to tell you, there's many days I wake up and I still think, what did I do? Because I love industry. I love my experience within Chevron. I tell my students, in my classes, I say, I wish my career upon you. Because I was so fortunate, I loved my experience inside the industry. In fact, I mimic industry still. I have many students who want to study with me not just because of the topic, because of my behaviors. I run my team in the university like I ran my team over in industry. So the same attitudes around leadership, transparency, respect, and so forth. I use that as a professor.

And I think people-- and you know what I think? I think if we use leadership skills as professors, won't we send out people who will be better leaders? Shouldn't we be examples of that? So anyway, what I'm trying to say here is that I love industry. In fact, I remember the moment. So I interviewed. And then, of course, I went through a process just like everybody did. There was interest in me, but I went through the fair interview system. And they-- I knew they were coming back with an offer. And I had a number on a piece of paper and that number was the minimum I could accept-- because, of course, it was a pay cut going to academia-- and I could still take care of my family. That matters to me, I've got three kids.


MICHAEL PYRCZ: The Chair of the department sent that number. And I actually, probably, made an audible, ugh, darn. Because there was a part of me that wanted them to be under, and then I would just have to be, sorry, I'll just have to keep being happy in industry. But no, they were right there at that number. And it was my wife who sat down with me afterwards, and said-- and I start to rationalize, I said, but no, we can't, it's a big move, it's all this. And she said, you will always regret, you will always wonder, what if? And so I made the choice.

So what I'm saying is, there is a parallel universe in which I'm still happy in industry. And I came to academia really driven by-- so when I was in industry, my attitude was this-- accept every challenge. And I did. I did a lot of interesting things because I said, I'll learn that. Like I learned C++ to take an internship with Chevron right at the beginning. I had done nothing in C++, and within a month I could do reasonable C++, that kind of thing, right?


MICHAEL PYRCZ: So to me, this was the ultimate accept every challenge. Could I be a professor?

VERN STEFANIC: Well, that just begs the question-- to jump out of sequence here, though. So what has been the biggest challenge that just kicked your tail, actually?

MICHAEL PYRCZ: I know. I know, I know. So I like the culture inside of industry. We win together. It's taken them two years to teach me that the Chair is not my boss. I still believe-- I feel like I'm in a hierarchical organization. I feel like everyone around me is on a team together. And my attitude-- I have 12 PhDs, six of them are co-supervised. And I'm more than happy to do that. What an honor to work with Dr. Torres-Verdin and Dr. Lake and just so many great professors, right?

OK. So academia-- I'm not saying anything negative, just to be clear here-- but that same focus on team, that same unified-- you in academia will feel alone. I'll tell you, I went to do some teaching at Noble Energy. And the second day of teaching, I went-- I woke up that morning, I started my car, and the car wouldn't start. And I realized, I'm just a professor here by myself with my personal car, it doesn't start. How the heck am I going to get there? How am I going to teach? How am I going to get my car fixed while I'm teaching all day? And that's when I realized, if I'd been working with Chevron, there's a card in my pocket, I'd phone it, everything's taken-- the company's got your back.


MICHAEL PYRCZ: You don't have that in academia. And so everything you do, you've got to be entrepreneurial. You've got to find funding.


MICHAEL PYRCZ: And people don't understand, one PhD, without overhead, is about $50,000 per year. And there are companies, many companies, that will nickel and dime you about $10,000 and $20,000 a year. And you're sitting there in the back of your mind realizing, this is just covering a term. You know what I'm saying? So I find that, to me, those are some of the greatest challenges. Now, are we going to ask about the positive stuff so I can end on a positive note?

VERN STEFANIC: Certainly. I was-- that's exactly what I was wondering. Yeah. What about the positives?

MICHAEL PYRCZ: The positives. So the days that I came home electrified, on fire, when I was in industry were the days that I was teaching and mentoring. My life is full of that now. I have an open door policy, students are coming in, I'm constantly mentoring and helping and assisting and teaching. I love teaching. I really enjoy standing up in front of a classroom and just teaching. I'll teach a new concept, like bootstrap is a good example. And I'll say, OK, today we're going to learn about statistical bootstrap, who's heard of this before? Nobody puts their hands up. And I'll look out and I'll say, this is truly a great day. I get to be the one to share this with you. I love that, that just gets me so excited, because this is changing people's worlds. And I have so many students-- can I brag?

VERN STEFANIC: Yeah. Oh, please.

MICHAEL PYRCZ: My rankings are really, really high. The rankings are very high, and the students love it. And I got so many students sending me messages of, this was the best class they took and they're so excited by it. And it really moves me. And to me, that just keeps me going. Because it doesn't matter what's difficult, when I get that, it just makes me feel so excited about what I'm doing. I feel validated about this choice.

VERN STEFANIC: I'm curious, because you do teach classes in industry as well, the difference-- two things-- the difference in your approach, if there is a difference, but also the difference in the people who you're talking to.

MICHAEL PYRCZ: Yeah. Yeah. That's a very good question. So one thing I do is-- well, when I teach in industry, I do recognize the fact I don't get as much time with the people. And so I high grade a lot more. And I do tend to be much more just practical. My attitude when I teach in industry is I want to teach you something in an hour or two that you can use tomorrow to show incremental value. That's my goal. And so I do a lot of that. I may cover some basic fundamentals and theory, but then I have to switch into practice pretty quickly.

With the students, you can evolve. You can grow that over time. And I do enjoy having a-- teaching a full term is-- it's amazing what you can cover in a full term, right? Now, I'll tell you one thing, though. I am a very nice person, but when it comes to being a lecturer and control of the classroom, I'm awful. I'm really awful. I will not tolerate unprofessional anything. In other words, your cell phones are away, if you've got a laptop-- and I roam that classroom-- and it's not on the course content, I'll kick you out. Because I have no tolerance for that, because guess what? You're going to leave my classroom knowing professional behavior.

And when I was a team leader, if you're sitting there texting someone in the middle of my meeting, it looks unprofessional. And so I use that when I teach, I have the professional industry standard. And I'm finding that's not universal. Students are surprised that, oh, I can't have my cell phone out, I can't be texting someone while I'm in your class?


MICHAEL PYRCZ: It amazes me. So anyway, it's-- yeah. I take the culture from industry into my classroom.

VERN STEFANIC: And there's so much-- I was thinking while you were telling that story. So I taught-- nothing like geoscience-- script writing for a while at a college in Tulsa. And I remember, maybe it was five, six years ago, all of a sudden-- and at first, I was really hacked off when they had their computers and everything. And then I realized that was their tool, they were actually using it. But they better be using it as a tool or else you get mad.

So machine learning-- in your analysis, then, is it a tool or is it the magic solution to something? How should we think of it?

MICHAEL PYRCZ: So that's a really good question. People need to manage their expectations with the technology. So one course I've really enjoyed to teach is I have a half a day course that I teach to executives in industry. It's basically-- I'm not going to say it's buzzword compliance-- but it's teaching them the highlights-- how would they judge this technology, how should they see the technology, what are some of the terms being used. And so I really enjoy teaching that. Because what I do is I spend about an hour just being very critical, very negative about it, showing how things can crash, how things don't work, when it could be abused and misused.

Now, what you'll find about me is I'm pretty flexible. I have a whole continuum of beliefs depending on the application. On one side, I'm quite optimistic that there's huge value and opportunity just to dramatically or make a significant shift or disruption to the way that we get the job done for the subsurface and geosciences and engineering. On the other side, I'm kind of pessimistic. And I see the limitations, I see how we're not going to be able to completely automate everything. And so, in general, my response would be, I'm never an advocate for the geoscientists in the box. I don't think we ever do. I go back to the idea of these methodologies will allow us to have geoscientists spend more time doing geoscience. And some of the more mundane parts of their job, their tasks, will basically be automated. Their interpretations would be better supported by systems that help them semi-automate their interpretations, help them put their information in, their knowledge in, at the same time, be aware of all the other potential contradictions with other information sources.

So in general, that's been my perspective as far as how these technologies are going to change and move us forward.

VERN STEFANIC: Yeah, because the deal-- I remember in your lecture you talked about it's an enormously high percentage of time that we're actually worrying about the data itself, right? What was it, like 80%?

MICHAEL PYRCZ: Often could be 90% of data preparation. Yes.

VERN STEFANIC: Yeah, right.

MICHAEL PYRCZ: Yes. And so, clearly, there's a lot we can do to automate and improve the efficiencies of what we do. And so I think there's huge value in that. At the same time, what's very interesting-- I was an a PhD defense, and I was co-supervising the student with Dr. Torres-Verdin. I think it was one of our co-supervised students. And I asked something about data analytics and machine learning, and he stopped and he said, listen, sometimes I feel like people use it as a crutch. And there's a great quote from Dr. Torres-Verdin where he said, people, instead of understanding the fundamental geoscience and engineering, will try to use data-driven approaches as a crutch.

That's my fear is that that will drive us in that direction. I don't think it should be that way. It's like I said during the talk, we have the first, the second and third scientific paradigms are essential to what we do. Geoscience and engineering knowledge is central to what we do. The fourth paradigm and data-driven approaches provide us a new toolkit, new methodologies we can use. Interpretability remains key.

What I find with my students is they often jump to the most complicated models. In my classes, they'll get so excited, they want to do the really complicated methodologies. They're like, look at me, I did this deep convolutional generative adversarial network, blah, blah, blah. And I built this model that made this prediction, it's got this r squared of variance explained of 90-something percent. And I'll go back and I'll look at it and I'll realize that this is over fit. This is just-- yeah, it's a model that has so many parameters in it and so little data to train it, you're just able to precisely honor the data. It has enough degrees of freedom to honor the data.

And so this has been one of my concerns, too. Now, at the same time, I am excited by the technology. I show an example in my talk where we're able to make forecasts of the intragranular velocity field of flow of a fluid, right? That's awesome. Now, where do I think that could play a role-- multi-scale modeling. Imagine we're trying to understand how poor scale cementation, diagenetic alterations affect overall recovery mechanisms at the large scale of the drainage radius of the well. Imagine if we had models that can work that fast in making those predictions across those small scales to larger scales. That would be beautiful. Because now we have the geoscientists sitting there saying, I think this is the diagenetic alterations, I think this is how the grain structure is stacked, I think this is the uncertainty range. And they can immediately see how that impacts the bottom line, which is dollars and flow, right?

VERN STEFANIC: Right. Right. OK. So let me ask you this question, then, with that in mind. So how is this going to impact the industry of the future?

MICHAEL PYRCZ: Yeah. So we had a great talk-- one of the Vice Presidents of Ecuador came and visited us at the department and gave an excellent talk about, basically, where Ecuador is going. And he made a lot of statements about how this new technology is going to be used, how the workforce is going to change. I think there will be changes. Now, my perspective is more of an evolution rather than a revolution. I was in front of a panel for Price Waterhouse Cooper, as I mentioned. And there was another individual expert there who was suggesting a massive replacement of engineers and geoscientists with data scientists. And I look at that-- or they are advocates of the geoscientists or the engineer in the box.

VERN STEFANIC: Which is, I think, where some of the fear and intimidation is coming from, right?

MICHAEL PYRCZ: And I look at that and I don't agree with that at all. I think looking forward-- first of all, I look at our business, and I know our business because I've worked in it for all these years. I know much of it, and I know the complexity. I have a respect for the scientific disciplines. I've sat and listened to Professor Pemberton talk about ichnofacies. I've listened to Henry Posamentier talk about how he can interpret and do analysis of seismic and seismic geomorphology. I've listened to Tim McHargue talking about sequence stratigraphy standing at the rocks in the Karoo Basin in South Africa. Morgan Sullivan in the Ross Formation-- and so on and on.

I know the complexity of what we do. And this is what I know-- the great innovations and value that's been added within our field is because of creativity. It's because of discovering new concepts. And no machine has ever done that. There's a perfect quote I have in one of my talks-- my lectures to executives-- where it talks about the fact that the Higgs Boson was discovered using a lot of data analytics and a lot of machine learning and a lot of data science behind it to support it. But the actual discovery was not data-driven. It was not based on those sciences. They support it. But the actual creativity to look for it, the actual development of the design, it didn't come from a machine.

So that's what I believe. I believe that we'll still need-- geoscience and engineering will be core to everything we do.

VERN STEFANIC: I want to ask a personal question in that light, then. That would be-- and this gets us back to the story you talked about when you were at the-- filling up your car in Canada, right? And I think there may be a connection with what you just said and that moment and this long arc you've been on, this journey, is your creativity and your creative approach to life. Where do you go for inspiration? And where does that creative drive come?

MICHAEL PYRCZ: So it's a very good question. I feel I'm in my element when I'm working with my hands, when I'm doing something difficult physically. And so I have kayaks-- I actually have eight kayaks-- in my garage. Come to Austin, come kayaking with me. I take people kayaking every weekend. I tell my students or other faculty, I'll be 8:00 in the morning, Saturday, show up at my house, come kayaking. And I take people all the time.

When we do a really hard kayaking trip, when you have physical exertion, when you're paddling into the wind and the waves are battering against you-- to me, that's beautiful. And, to me, that teaches me something and it makes me appreciate something. Long hikes, climbing things, mountain biking things, doing things on my mountain bike I didn't think I could do. My wife and I, we do some weight training. I know I'm a skinny guy, but we still do a little bit to stay healthy and strong-- keep our bones strong. When you're lifting something heavier than you thought you could-- to me, there's beauty in that.

And so sometimes when I talk to my students-- my graduate class, my very first graduate class, was pretty small. It didn't sell out. I was a brand new professor, nobody knew me that first fall. There was only six students in it. I took everyone kayaking. I love it. I love it. I think that's-- I think part of education and sharing is sharing some of these things. I also do talk to students about work-life balance. I talk to them about how to succeed in industry. Those are other topics. But I do find that that's-- and then, of course, besides that, it's in those moments you pick up a guitar and just strum something and play away at something. It's just the feeling of the vibration and hearing the music you create, or attending music. So much beauty.

VERN STEFANIC: There's much beauty. And you see much beauty, so this ought to be an easy one to end with, and that's looking forward. And what possibilities do we still have? Have you tried to-- and you've said it yourself, that everything is changing so quickly and we're all learning together at the same time. So this may be difficult. On the other hand, you're a pretty optimistic person. What do you see going forward? What kind of expect-- what kind of changes might we anticipate?

MICHAEL PYRCZ: Yeah. Good question, good question. So there was a great talk given by my old PhD advisor on black boxes. And the comment was that black box use is not a bad thing. How many people truly understand their cell phone? Because the cell phone rate, the technology behind the cell phone, none of us really understand it-- many of us don't. But we use it, and we make use of it, and we're productive with it. The idea of the maturing of some of these technologies to the point where they become black box and extremely useful-- that, to me, excites me. The idea of huge, new ways that we can interface with data and with systems and understand and explore systems, new lenses to be able to see what's going on in the subsurface. That's exciting to me.

I truly believe that this technology will just make it more and more exciting. It's a wonderful time to be a geoscientist. It's an amazing time to see this transition. I think we're going to like what we'll see.

VERN STEFANIC: And I can't wait to be there with you on that. Thank you.


VERN STEFANIC: Thank you, Michael, for being with us today.

MICHAEL PYRCZ: Appreciate it.

VERN STEFANIC: We've been talking today to Dr. Michael Pyrcz, an associate professor in the Cockrell School of Engineering at the University of Texas at Austin. Be sure to check out his lecture at AAPG.org. Believe me, you won't be sorry. And watch this space for more AAPG podcasts that will cover a variety of important subjects and intriguing people, including our ongoing Digging Deeper series, featuring conversations with this year's Distinguished Lecturers.

All AAPG podcasts are available through the AAPG website or your favorite podcast platform-- iTunes, Spotify, Stitcher, Google-- whatever your preference, we're they're. The Distinguished Lecture program is a jointly operated program by AAPG and the AAPG Foundation. And, as always, we hope you'll take a moment soon to check out the AAPG Foundation website to discover how you can be part of ensuring the future of geosciences. For now, thanks for listening.

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