SEG 2019 – San Antonio

SEG 2019 – San Antonio

Geophysical Insights is featuring two new off-the-shelf, deep learning applications in the Paradise® AI workbench

Seismic Facies Classification and Fault Detection


SEG 2019, 15-20 September
Booth #1038

Solving interpretation problems with deep learning and machine learning technologies

Visit booth #1038 to hear industry thought leaders present applications of machine learning and deep learning technologies to seismic interpretation in different geologic settings.  Learn how technologies like GPU processing will change and enable geoscience workflows.  Here are a few of the topics:

  • Seismic facies classification using deep learning
  • Net reservoir discrimination through multi-attribute analysis
  • Thin bed detection with unsupervised machine learning
  • Case studies of machine learning in geologic settings
  • GPU processing for attribute generation



Geophysical Insights will be hosting daily lunch & learns at booth 1038 with featured talks by Fabian Rada, Dr. Tom Smith, and Dustin Dewett. Register for the lunch & Learns with the form on this page.

Gain Access to 2019 SEG Booth Presentations or Register for a Lunch & Learn

Speakers & Presentations

Dustin Dewett

Product Manager, Geophysical Insights
Seismic Facies Classification and Fault Detection using Deep Learning

Mike Dunn

SVP of Business Development, Geophysical Insights
Generating Attributes on GPUs

Reynaldo Gomez

Energy Account Manager, NVIDIA
Generating Attributes on GPUs

Dr. Bob Hardage

Geoscience Adviser, Geophysical Insights
Machine Learning Geobodies in the Wolfberry Play of the Permian Basin

Exposing Karst Topography in the Deep Ellenberger of the Permian Basin through Machine Learning

Dr. Carrie Laudon

Senior Geoscientist, Geophysical Insights
Machine Learning Improves Stratigraphic Resolution in the Niobrara

Dr. Ivan Marroquin

Senior Research Geophysicist, Geophysical Insights
Studies in Automated Optimization for Multi-Attribute Classification

Fabian Rada

Senior Adviser to PEMEX, Petroleum Oil & Gas Services
Net Reservoir Discrimination through Multi-Attribute Analysis at Single Sample Scale

Rocky Roden

Senior Geophysicist, Geophysical Insights
Comparing Machine Learning Methods and the Black Box Perception

Machine Learning on the Geoscience Technology Adoption Cycle

Deborah Sacrey

Senior Geoscientist, Geophysical Insights
A Journey through Paradise – Case Histories in Different Depositional Environments

Dr. Tom Smith

President & CEO, Geophysical Insights
Thin Bed Detection with Unsupervised Machine Learning

Mathematical Foundation for Machine Learning of Multi-Attribute Seismic Surveys

For a printable version of the schedule, click here

Presentation Schedule

Time Monday, 16 Sep Tuesday, 17 Sep Wednesday, 18 Sep
9:30-10:00 Exhibition opens at 10 AM Deborah Sacrey: A Journey through Paradise – Case Histories in Different Depositional Environments Deborah Sacrey: A Journey through Paradise – Case Histories in Different Depositional Environments
10:30-11:00 Bob Hardage: Machine Learning Geobodies in the Wolfberry Play of the Permian Basin

Rocky Roden: Comparing Machine Learning Methods and the Black Box Connotation

Carrie Laudon: Machine Learning Improves Stratigraphic Resolution in the Niobrara
12:00-1:00 Fabian Rada: Net Reservoir Discrimination through Multi-Attribute  Analysis at Single Sample Scale Tom Smith: Thin Bed Detection with Unsupervised Machine Learning Dustin Dewett: Seismic Facies Classification and Fault Detection using Deep Learning
1:30-2:30 Mike Dunn and Reynaldo Gomez: Generating Attributes on GPUs Carrie Laudon: Machine Learning Improves Stratigraphic Resolution in the Niobrara Rocky Roden: Machine Learning on the Geoscience Technology Adoption Cycle
3:00-4:00 Tom Smith:  Mathematical Foundation for Machine Learning of Multi-Attribute Seismic Surveys Dustin Dewett: Seismic Facies Classification and Fault Detection using Deep Learning Bob Hardage: Exposing Karst Topography in the Deep Ellenberger of the Permian Basin through Machine Learning
4:30-5:30 Dustin Dewett: Seismic Facies Classification and Fault Detection using Deep Learning Ivan Marroquin: Studies in Automated Optimization for Multi-Attribute Classification Exhibition closes at 4:30 PM

Machine Learning with Deborah Sacrey – AAPG Energy Insights Podcast

Machine Learning with Deborah Sacrey – AAPG Energy Insights Podcast

One of our very own esteemed geoscientists, Deborah Sacrey, sat down with Vern Stefanic to talk about Machine Learning in the energy industry.

To watch the video, please click here.

Transcript of podcast

Full Transcript

VERN STEFANIC: Hi, I’m Vern Stefanic. And welcome to another edition of AAPG’s Podcast, Energy Insights, where we talk to the leaders and the people of the energy industry who are making things happen and bringing the world more energy, the energy that it needs to keep going.

Today, we’re very happy to have as our guest Deborah Sacrey, who is Auburn Energy, consultant working outside of Houston, Texas. But somebody who’s got experience working in the energy industry for a long time, who’s been through many changes in the industry, and who keeps evolving to find new ways to make herself valuable to the profession and to the industry that’s going forward. Deborah, welcome, and thank you for being here with us today.

DEBORAH SACREY: I’m delighted. Thank you so much for inviting me.

VERN STEFANIC: Well one reason– we’re doing this from the AAPG Annual Convention in San Antonio, where you have been one of the featured speakers. And you were talking about what the future of petroleum geologist is going to be. Which was perfect, because you found yourself somebody who’s had to sort of evolve and change your focus, the focus of your career several times. Could you tell us a little bit about your journey?

DEBORAH SACREY: Well, what I found is that every time there’s a major technology change, a paradigm shift in the way we look at data, there are consequences to that. There are benefits and consequences. If you’re not prepared to accept that technology change, you get left behind. And it makes it hard for you to find a job.

But if you accept that technology change, and embrace it, and learn about it, then you can morph yourself into a very successful career, until the next time the technology changes again. So you’re constantly– you have ups and downs, and you’re constantly morphing yourself and evolving yourself to embrace new technology changes as they come along.

VERN STEFANIC: Which is important, because we live in– in the industry right now, there have been rapid change, which we’re going to talk about some of the places where we’re going on that. But because of that we always hear stories of a lot of petroleum geologists or professional geoscientists, who find themselves awkwardly lost in the shuffle somewhere and not knowing what to do. What I love about your message is that it’s the understanding of how technology is driving all of this and being aware of that. You’ve experienced this several times in your career, is that right?

DEBORAH SACREY: Oh, absolutely. I’ve been– I got out of school in 1976. So this makes my 43rd year in the career. What I’ve gone through is, we had a digital transformation. When I got out of school, we were always looking at paper seismic records. And I went to work for Gulf, and they’d be rolled up every night, and they’d be put in a tube, and they’d be locked behind the door. And during the day, you’d go check them out and take them to your office and work on paper.

So the digital transformation is when we moved from paper records into workstations, where we could actually scale the seismic, and can see the seismic, and blow it up, and do different things with it. That was a huge transformation. When we went from paper logs, which a lot of people still use today, to something that you can see on the screen, and blow it up and see all the nuances, of the information in the well.

Then the next major transformation came in the middle ’90s, when software was available for the smaller clients and independents to start looking at 3D. So we transition from the 2D world into the 3D world. And that was huge. I mean, it’s amazing to me that there’s any space left on the Gulf Coast that doesn’t have a 3D covering yet at this point. And now, we’re getting ready to go into another major transformation. And it’s all about data.

VERN STEFANIC: People have been told that, I think, maybe a couple of times, that oh, yeah, I understand that I have to change, and I have to be aware of it. But they really not have the skills or the insight on how to make some of those changes happen. I’m just curious, in your career do you recall some of the ways that you had to– just some of the realizations that you had. First, not just that you had to change, but some of the steps that you did to make it happen.

DEBORAH SACREY: Well, I think a lot of it, and what was important to me, is when I could see the changes coming. I had to educate myself. I didn’t have a resource to go to. I wasn’t working for a big company that had– that would send you off to classes. So it’s a matter of doing the research and understanding the technology that you’re facing.

And what I told people yesterday in my talk is that the AAPG Convention or any convention is an excellent resource for free education. Go out and look what the vendors are trying to do. And that’s your insight into how the technology is changing. And people can walk around the convention center. And they can listen to presentations for free and try to get an inkling of what’s getting ready to happen.

VERN STEFANIC: That’s great advice. That’s great insight. By the way, I’ve noticed that too in myself, in walking around the convention floor. That’s where I heard many things for the first time. Thought, oh, when I was at the Explorer, thought, oh, I ought to do a story about this.

DEBORAH SACREY: Right, exactly. And I think it’s especially important for the young people, the early career people, or the kids coming out of school, to understand that their lives will not always be with one company. When I went to work for Gulf in 1976, the gentleman who interviewed me on campus, looked me in the eye and said, Gulf will be your place of employment for life.

And I referenced yesterday a really good book. It’s called Who Moved my Cheese. So our cheese in our careers is constantly getting moved. And we have to be able to accept that and adapt to it. And you can only do it through education.

VERN STEFANIC: When did you realize, or was there a moment, when you saw that, oh, big data is important? Because it seems very obvious that we would see that. But I’m not sure everybody clicks on to– not just big data is going to be the name of the game, but this is what I’m going to do about it. What was your experience with that?

DEBORAH SACREY: Well, in 2011, a gentleman whom I’d been working with for a long time, Tom Smith, and he was the– Dr. Smith was the guy who started S&P, or Kingdom. When he sold Kingdom, he started doing research into ways that we could extend our understanding of seismic data and do applications using seismic attributes.

So he brought me in to help work with the developers to make this software geoscience friendly. Because our brains are wired a little bit differently from other people, other industries. And the technology he was using is machine learning, but it’s cluster analysis and it’s pattern recognition. Now what’s happened in the big data world is, all these companies, all the majors, all the large independents, have been drilling wells for years. And a lot of times, they’ve just been shoving the logs, and the drilling reports, and everything in a file.

So that’s all this paper that’s out there, that they’re just now starting to digitize, but you have to get it in a way that’s easily retrievable. So the big data– every time you drill a well now, you’re generating 10 gigabytes of information. And think about the wells that are being drilled, and how that information is being organized, and how it’s being put in– so if you use a keyword, like 24% porosity, you can go in and retrieve information on wells where they’ve determined that there’s 24% porosity in reservoirs. And that’s some of the data transformation we’re getting ready to go through, to make it accessible, because there’s so much out there.

VERN STEFANIC: OK, so understanding that having data is the key to having more knowledge, is the key to actually being a success, not just with your company, but also with actually bringing energy to the world.

DEBORAH SACREY: Right, I mean it’s not getting any easier to find. So we’re having to use advanced methods of technology and understanding the data information to be able to find the more subtle traps.

VERN STEFANIC: So– and I don’t know if this is too much of a jump– in fact, we can fill in the blanks if it is– but today we’re talking about machine learning and its applications and implications for the energy industry. And I know you are somebody who has been a little bit ahead of the curve on this one, in recognizing the need to understand what this is all about. So for some of us who don’t understand like you do, could you talk a little bit about that?

DEBORAH SACREY: Well, I can be specific about the technology that I’ve been using for the last five years.


DEBORAH SACREY: And like I said earlier, Tom brought me in to help guide the developers. But the basics behind the software I’ve been using is that instead of looking at the wavelet in the seismic data, I’m parsing the data down to a sample level. I’m looking at sample statistics.

So if your wavelet, if you’re in low frequency data, and your wavelet’s 30 milliseconds between the trough and the peak, I may be looking at 2 millisecond sample intervals. So I’m parsing the data 15 times as densely as you would if you were looking at the wavelet. What this allows me to do is, it allows me to see very thin beds at depth. Because I’m not looking at conventional seismic tuning anymore. I’m looking at statistics and cluster analysis that comes back to the workstation. Because every sample has an X, Y, and Z. So it has its place in the earth. And then I’m looking at true lithology patterns, like we’ve never been able to see before.

VERN STEFANIC: OK, well, never been able to see before is a remarkable statement. Are we talking about a game change for the profession at this point?

DEBORAH SACREY: Most definitely. I give a lot of talks on case histories. I’ve probably worked on a hundred 3Ds in the last five years, all around the world. And I have one example in the Eagle Ford set. The Eagle Ford is only a 30 millisecond thick formation in most of Texas. And so you’re looking at a peak and a trough, and you’re looking at two zero crossings. That’s four sample points.

But when you’re looking at that kind of discrete information that I can get out of it, I can see all six facies strats from the clay base, up through the brittle zone and the ash top, right underneath the Austin Chalk. Well, it’s the brittle zone, in the middle, where the higher TOC is, and what people are trying to stay in when they’re drilling the Eagle.

If you can define that and you can isolate it, then you can geosteer better. You can get better results from your well. But you’re talking about something that’s only 150 feet deep. And you’re trying to discern a very special part of that, where the hydrocarbons are really located. And so that’s going to be a game-changer.

VERN STEFANIC: So what would you say to people– but this is still you– you’re bringing your skills, your talents, everything that has brought you to this point in your career, and applying them with this new technology. What about the criticism, which may be completely invalid, but what about the criticism that people say that because of machine learning, we’re headed to a place where the very nature of the jobs of the professional geologists are going to be threatened? Is that a possibility? Is that something that we should even think about?

DEBORAH SACREY: Well, I think it’s a possibility. And why I say that is because a lot of the machine learning applications that are being developed out there are really improving efficiency, especially when it comes to the field and monitoring pressure gauges and things like that. They’re doing it remotely. And they’re getting into the artificial intelligence aspect of it. But the efficiency that you can bring to the field and operations will get rid of some of the people who go down and check the wells every day. Because they’ll be able to monitor it– they’ll know when rest is getting to one day or whether bad weather comes through and they’ve had problems. They’ll be able to know immediately without having to send someone out to the field.

Now, when you relate it to the geosciences, especially on the seismic side, you’re going to still need the experience. Because it’s a matter of maybe having a different way to view the data, but someone’s still going to have to interpret it. Someone’s still going to have to have knowledge about the attributes to use in the first place. That takes a person with some experience. And it’s not something that’s usually learned overnight. So I think some aspects of it will improving efficiency in the industry and do get rid of some jobs, and then some other aspects will not get rid of jobs.

VERN STEFANIC: Well, let me go down a difficult path then in our conversation, we all are aware of demographics within the industry, within the profession. And so, let’s start first with the baby boomers. Right, so there is an example. We have a case history of how we can approach that. From your perspective, though, it’s actually just being aware that change is necessary.

DEBORAH SACREY: Yeah, and you know, there are a lot of people out there who are in denial. And they think they can keep on working that same square of earth all the rest of their career. And those will be the guys who get left behind. One of the things I tried to emphasize yesterday is that old dogs can learn new tricks. And this is not that hard.

This kind of technology has been on Wall Street, it’s been in the medical industry for years. We’re just now getting to the point where we’re applying it to the oil and industry, to the energy industry.

VERN STEFANIC: Is there any advice that you could give to maybe younger, mid-career, the Gen X, or even kind of YPs, who are just now getting into the industry, special things they should be looking for or trying to do to enhance their careers?

DEBORAH SACREY: Well, certainly, if they’re working for a large company, American companies have already started making the shift to machine learning or artificial intelligence data mining. I’ve done a lot of work with Anadarko. They put a whole business unit together some years ago specifically to look into methodologies to improve the efficiencies on how they can get more out of this data. All the big companies have research departments. They’re getting into it.

I have a friend who just got a PhD several years ago in data mining. And she said her company is looking and screening all the new resumes coming in for any kind of statistics, any kind of data mining technology, or any kind of advanced machine learning. And they need a reference that they’ve had exposure to it, but that’s becoming a discriminator for finding a job in some instance, because they’re all making the digital transformation to efficiency and machine learning.

VERN STEFANIC: I don’t want to– I don’t want to overlook what might be obvious to some people, but I’d like to put it on the record. Auburn Energy, you, in recognizing and embracing the need to evolve along with the industry, as technology changes, you’ve had a little bit of success at this.

DEBORAH SACREY: I’ve been very lucky. I’ve been blessed in life with the successes I’ve had. I was getting bored with mapping and 3D. And so several years ago, at the time I was involved in this, I started looking in to different attributes, and what kind of reactions to the rock properties and sizing to get these different attributes, which is why this machine learning technology came along at the right time for me. Because it’s just a gradual going on. I’m not looking at one attribute at a time, I’m looking at 10 at the same time.

And in doing so, and in looking at the earth in a different way, I’ve been able to pick up some nuances that people have missed and had discoveries. I had a two million barrel field I found a couple years ago. I had an 80 bcf field that I found a couple years ago. I just had a discovery in Mississippi and in Oklahoma, in southern Oklahoma. And we’re expanding our lease activities to pick up on what I’m seeing in my technology there. So not only has it revitalized my love of digging in and looking at seismics, but it proves to be profitable as well.

VERN STEFANIC: So let me go ahead and maybe put you on the spot. Don’t mean to be– but because you are a person who’s gone through many stages of the industry, what can you see happening next? Do you have any kind of crystal ball look out to– or even just to say this is what needs to happen next to help you do your job better.

DEBORAH SACREY: Well, certainly the message I’m trying to get out to people of all ages is that this paradigm shift that we’re getting ready to go through, and you hear it over and over at all the conventions, is going to substantially change their lives. And we need to get on the train before it leaves the station or they will be left behind. And each time we’ve had a major paradigm shift, there have been some people who’ve been reluctant or didn’t want to get outside their box. And they wake up five or six years later and don’t recognize the world. Their world has completely changed and people have moved on.

And each time that happens, you can lose a certain part of the brain power and people who have knowledge of one county and one piece of Texas, because they just didn’t want to make– they didn’t want to bother themselves. And so I’m trying to get the word out to people that this change is coming. And it’s something that can be easily embraced and you should not be afraid of it, and just get on the bandwagon. I mean, it’s not that hard.

The technology and the software that I’m seeing being developed out there, it’s a piece of cake to use. You just have to have some knowledge of the seismic or logs. There’s a technology called convolutional neural network and it’s being used to map faults through 3D. So you may go in and map the faults in 10 lines out of 80 blocks of actual data. And the machine goes in and learns what certain kinds of faults look like from the 10 lines that you’ve mapped. And it will finish mapping all the faults in the whole bunch of blocks in the offshore data.


DEBORAH SACREY: It’s scary. But fault picking is like one of the most boring things we can do in seismic data. So if you can find– if you can find an animal out there that will crawl through that data, and pick out the faults for you, that’s wonderful. That saves tons of human hours. And it’s good for stratigraphy. You give it some learning lines where you’ve mapped out blocks of clastics or carbonates, or turbidites, something like that, and it learns from that. And then it goes and maps that stratigraphy anywhere it can find it in the 3D. It’s very unique. You need to start educating yourself about what’s out there.

VERN STEFANIC: Well, you’re absolutely right. I try to in the world that I work with, but I’m always impressed that in the world that you’re part of, that there’s so much change that keeps coming. And it’s just fast. And it’s again, and again, and again. And the ability that people, such as yourself, has had to embrace that and to use technology in the new way– in fact, I’m going to guess– I’m going to guess that– have you offered suggestions to anyone, who are developing technology, have you got to the point where you say, you know what we need, we need now for it to do this?

DEBORAH SACREY: I’m still on a development team for the software I’m using.

VERN STEFANIC: You’re on the development– OK.

DEBORAH SACREY: Yeah, so we’re forward thinking two years down the road what kind of– what can we anticipate the technology needs to be doing two to three years down the road.

VERN STEFANIC: Can you talk about any of that?

DEBORAH SACREY: Well, I mean, I can. And certainly, this CNN technology is part of it. We’ve been approached by several larger companies to put this into our software. And they’re willing to help pay for the effort to do that, because it would take their departments too long. We’re too far advanced where we are. And it would take them too long to recreate the wheel.

So they’d rather support us to get the technology that they need, that they need for their data. And the beauty of all this is that you don’t have to shoot anything new. You don’t even necessarily have to reprocess it. You’re just getting more out of it than you’ve ever been able to get before.

VERN STEFANIC: That is beauty.

DEBORAH SACREY: It is cool. Because a lot of people don’t have the money to go shoot more data or reprocess it. They just want to take advantage of the stuff they already have in their archives.

VERN STEFANIC: When people talk about the industry being a sunset industry, I think they’re not giving it proper credit for what’s going on.

DEBORAH SACREY: Oh, I see this totally revitalizing– one of the examples I showed yesterday was the two million barrel oil field that I found in Brazoria County, Texas. And it’s from a six-foot thick off-shore bar at 10,800 feet. Well, that reflector is so weak– I mean, it’s not a bright spot. It doesn’t show up. People would have ignored it a long time, and have ignored it a long time, for drilling.

But I can prove that there’s two million barrels of oil in that six-foot thick sand that covers about 1,900 acres. So how many of those little things that we’ve ignored for years and years are still out there to be found. That’s what I’m saying. This technology is going to give us another little push. It’ll make us more efficient in the unconventional world. It will definitely help us find the subtle traps in the conventional world.

VERN STEFANIC: So there you have it. If you’re part of this profession now, you’re part of this industry now, don’t be discouraged. There’s actually great work to be done.

DEBORAH SACREY: Oh, there’s a lot of stuff left to find. We haven’t begun to quit finding yet. I mean, it’s just like Oklahoma– I grew up in Oklahoma. And for years and years, all the structural trap had been drilled, and all the clays had gone through. And everyone said, well, Oklahoma’s had it. And we turn around and there’s a new play. And you turn around, there’s the unconventional. There’s the SCOOP and STACK. There’s all the Woodford. There’s all these things that reenergized Oklahoma. And it’s been poked and punched for over 100 years. And people are still finding stuff. So we just– we just have to put better glasses on.


DEBORAH SACREY: We have to sharpen our goggles. And get in there and see what’s left.

VERN STEFANIC: Great words. Deb, thanks for this conversation today.

DEBORAH SACREY: You’re welcome.

VERN STEFANIC: Thank you. I hope it’s a conversation that we’ll continue. We’ll continue having this talk, because it sounds like there’s going to be new chapters added to the story.

DEBORAH SACREY: Oh, yeah, and I’m really– you know, I’m 66 years old, but I’m not ready to give it up yet. I’m having way too much fun.

VERN STEFANIC: That’s great. Thank you.

DEBORAH SACREY: You’re welcome.

VERN STEFANIC: And thank you for being part of this edition of Energy Insights, the AAPG Podcast, coming to you on the AAPG website, but now coming to you on platforms wherever you want to look. Look up a AAPG Energy Insights, we’ll be there. And we’re glad you’re part of it. But for now, thanks for listening.

The Oil Industry’s Cyber–Transformation Is Closer Than You Think

The Oil Industry’s Cyber–Transformation Is Closer Than You Think

By David Brown, Explorer Correspondent
Published with permission: AAPG Explorer
June 2019

The concept of digital transformation in the oil and gas industry gets talked about a lot these days, even though the phrase seems to have little specific meaning.

So, will there really be some kind of extensive cyber-transformation of the industry over the next decade?

“No,” said Tom Smith, president and CEO of Geophysical Insights in Houston.

Instead, it will happen “over the next three years,” he predicted.

Machine Learning

Much of the industry’s transformation will come from advances in machine learning, as well as continuing developments in computing and data analysis going on outside of oil and gas, Smith said.

Through machine learning, computers can develop, modify and apply algorithms and statistical models to perform tasks without explicit instructions.

“There’s basically been two types of machine learning. There’s ‘machine learning’ where you are training the machine to learn and adapt. After that’s done, you can take that little nugget (of adapted programming) and use it on other data. That’s supervised machine learning,” Smith explained.

“What makes machine learning so profoundly different is this concept that the program itself will be modified by the data. That’s profound,” he said.

Smith earned his master’s degree in geology from Iowa State University, then joined Chevron Geophysical as a processing geophysicist. He later left to complete his doctoral studies in 3-D modeling and migration at the University of Houston.

In 1984, he founded the company Seismic Micro-Technology, which led to development of the KINGDOM software suite for integrated geoscience interpretation. Smith launched Geophysical Insights in 2009 and introduced the Paradise analysis software, which uses machine learning and pattern recognition to extract information from seismic data.

He’s been named a distinguished alumnus of both Iowa State and the University of Houston College of Natural Sciences and Mathematics, and received the Society of Exploration Geophysicists Enterprise Award in 2000.

Smith sees two primary objectives for machine learning: replacing repetitive tasks with machines – essentially, doing things faster – and discovery, or identifying something new.

“Doing things faster, that’s the low-hanging fruit. We see that happening now,” Smith said.

Machine learning is “very susceptible to nuances of the data that may not be apparent to you and I. That’s part of the ‘discovery’ aspect of it,” he noted. “It isn’t replacing anybody, but it’s the whole process of the data changing the program.”

Most machine learning now uses supervised learning, which employs an algorithm and a training dataset to “teach” improvement. Through repeated processing, prediction and correction, the machine learns to achieve correct outcomes.

“Another aspect is that the first, fundamental application of supervised machine learning is in classification,” Smith said,

But, “in the geosciences, we’re not looking for more of the same thing. We’re looking for anomalies,” he observed.

Multidimensional Analysis

The next step in machine learning is unsupervised learning. Its primary goal Is to learn more about datasets by modeling the structure or distribution of the data – “to self-discover the characteristics of the data,” Smith said.

“If there are concentrations of information in the data, the unsupervised machine learning will gravitate toward those concentrations,” he explained.

As a result of changes in geology and stratigraphy, patterns are created in the amplitude and attributes generated from the seismic response. Those patterns correspond to subsurface conditions and can be understood using machine-learning and deep-learning techniques, Smith said.

Human seismic interpreters can see only in three dimensions, he noted, but the patterns resulting from multiple seismic attributes are multidimensional. He used the term “attribute space” to distinguish from three-dimensional seismic volumes.

In geophysics, unsupervised machine learning was first used to analyze multiple seismic attributes to classify these patterns, a result of concentrations of neurons.

“We see the effectiveness of (using multiple) attributes to resolve thin beds in unconventional plays and to expose direct hydrocarbon Indicators in conventional settings. Existing computing hardware and software now routinely handle multiple-attribute analysis, with 5 to 10 being typical numbers,” he said.

Machine-learning and deep-learning technology, such as the use of convolutional neural networks (CNN), has important practical applications in oil and gas, Smith noted. For instance, the “subtleties of shale-sand fan sequences are highly suited” to analysis by machine learning-enhanced neural networks, he said.

“Seismic facies classification and fault detection are just two of the important applications of CNN technology that we are putting into our Paradise machine-learning workbench this year,” he said.

A New Commodity

Just as a seismic shoot or a seismic imaging program have monetary value, algorithms enhanced by machine-learning systems also are valuable for the industry, explained Smith.

In the future, “people will be able to buy, sell and exchange machine-learning changes in algorithms. There will be industry standards for exchanging these ‘machine-learning engines,’ if you will,” he said.

As information technology continues to advance, those developments will affect computing and data analysis in oil and gas. Smith said he’s been pleased to see the industry “embracing the cloud” as a shared computing-and-data-storage space.

“An important aspect of this is, the way our industry does business and the way the world does business are very different,” Smith noted.

“When you look at any analysis of Web data, you are looking at many, many terabytes of information that’s constantly changing,” he said.

In a way, the oil and gas industry went to school on very large sets of seismic data when huge datasets were not all that common. Now the industry has some catching up to do with today’s dynamic data-and-processing approach.

For an industry accustomed to thinking in terms of static, captured datasets and proprietary algorithms, that kind of mind-shift could be a challenge.

“There are two things we’re going to have to give up. The first thing is giving up the concept of being able to ‘freeze’ all the input data,” Smith noted.

“The second thing we have to give up is, there’s been quite a shift to using public algorithms. They’re cheap, but they are constantly changing,” he said.

Moving the Industry Forward

Smith will serve as moderator of the opening plenary session, “Business Breakthroughs with Digital Transformation Crossing Disciplines,” at the upcoming Energy in Data conference in Austin, Texas.

Presentations at the Energy in Data conference will provide information and insights for geologists, geophysicists and petroleum engineers, but its real importance will be in moving the industry forward toward an integrated digital transformation, Smith said.

“We have to focus on the aspects of machine-learning impact not just on these three, major disciplines, but on the broader perspective,” Smith explained. “The real value of this event, in my mind, has to be the integration, the symbiosis of these disciplines.”

While the conference should appeal to everyone from a company’s chief information officer on down, recent graduates will probably find the concepts most accessible, Smith said.

“Early-career professionals will get it. Mid-managers will find it valuable if they dig a little deeper into things,” he said.

And whether it’s a transformation or simply part of a larger transition, the coming change in computing and data in oil and gas will be one of many steps forward, Smith said.

“Three years from now we’re going to say, ‘Gosh, we were in the Dark Ages three years ago,’” he said. “And it’s not going to be over.”