web analytics
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.

Solving Interpretation Problems using Machine Learning on Multi-Attribute, Sample-Based Seismic Data

Solving Interpretation Problems using Machine Learning on Multi-Attribute, Sample-Based Seismic Data

Solving Interpretation Problems using Machine Learning on Multi-Attribute, Sample-Based Seismic Data
Presented by Deborah Sacrey, Owner of Auburn Energy
Challenges addressed in this webinar include:

  • Reducing risk in drilling marginal or dry holes
  • Interpretation of thin bedded reservoirs far below conventional seismic tuning
  • How to better understand reservoir characteristics
  • Interpretation of reservoirs in deep, pressured environments
  • Using the classification process to help with correlations in difficult stratigraphic or structural environments

The webinar is open to those interested in learning more about how the application of machine learning is key to seismic interpretation.

Deborah Sacrey

Deborah Sacrey


Auburn Energy

Deborah Sacrey is a geologist/geophysicist with 41 years of oil and gas exploration experience in the Texas, Louisiana Gulf Coast, and Mid-Continent areas of the US. Deborah specializes in 2D and 3D interpretation for clients in the US and internationally.

She received her degree in Geology from the University of Oklahoma in 1976 and began her career with Gulf Oil in Oklahoma City. She started Auburn Energy in 1990 and built her first geophysical workstation using the Kingdom software in 1996. Deborah then worked closely with SMT (now part of IHS) for 18 years developing and testing Kingdom. For the past eight years, she has been part of a team to study and bring the power of multi-attribute neural analysis of seismic data to the geoscience community, guided by Dr. Tom Smith, founder of SMT. Deborah has become an expert in the use of the Paradise® software and has over five discoveries for clients using the technology.

Deborah is very active in the geological community. She is past national President of SIPES (Society of Independent Professional Earth Scientists), past President of the Division of Professional Affairs of AAPG (American Association of Petroleum Geologists), Past Treasurer of AAPG and Past President of the Houston Geological Society. She is currently the incoming President of the Gulf Coast Association of Geological Societies (GCAGS) and is a member of the GCAGS representation on the AAPG Advisory Council. Deborah is also a DPA Certified Petroleum Geologist #4014 and DPA Certified Petroleum Geophysicist #2. She is active in the Houston Geological Society, South Texas Geological Society and the Oklahoma City Geological Society (OCGS).

Solving Exploration Problems with Machine Learning

Solving Exploration Problems with Machine Learning

By: Deborah Sacrey and Rocky Roden
Published with permission: First Break
Volume 36, June 2018


Over the past eight years the evolution of machine learning in the form of unsupervised neural networks has been applied to improve and gain more insights in the seismic interpretation process (Smith and Taner, 2010; Roden et al., 2015; Santogrossi, 2016: Roden and Chen, 2017; Roden et al., 2017). Today’s interpretation environment involves an enormous amount of seismic data, including regional 3D surveys with numerous processing versions and dozens if not hundreds of seismic attributes. This ‘Big Data’ issue poses problems for geoscientists attempting to make accurate and efficient interpretations. Multi-attribute machine learning approaches such as self-organizing maps (SOMs), an unsupervised learning approach, not only incorporates numerous seismic attributes, but often reveals details in the data not previously identified. The reason for this improved interpretation process is that SOM analyses data at each data sample (sample interval X bin) for the multiple seismic attributes that are simultaneously analysed for natural patterns or clusters. The scale of the patterns identified by this machine learning process is on a sample basis, unlike conventional amplitude data where resolution is limited by the associated wavelet (Roden et al., 2017).

Figure 1 illustrates how all the sample points from the multiple attributes are placed in attribute space where they are standardized or normalized to the same scale. In this case, ten attributes are employed. Neurons which are points that identify the patterns or clusters, are randomly located in attribute space where the SOM process proceeds to identify patterns in this multi-attribute space. When completed, the results are nonlinearly mapped to a 2D colormap where hexagons representing each neuron identify associated natural patterns in the data in 3D space. This 3D visualization is how the geoscientist interprets geological features of interest.

Figure 1. Illustration of the SOM process where samples from ten seismic attributes are placed in attribute space, normalized, then 64 neurons identify 64 patterns from the data in the SOM process. The interpreter selects one or a combination of neurons from the 2D colourmap to identify geologic features of interest.

The three case studies in this paper are real-world examples of using this machine learning approach to make better interpretations.

Case History 1 – Defining Reservoir in Deep, Pressured Sands with Poor Data Quality

The Tuscaloosa reservoir in Southern Louisiana, USA, is a low-resistivity sand at a depth of approximately 5180 to 6100 m (17,000 to 20,000 ft). It is primarily a gas reservoir, but does have a component of liquids to it as well. The average liquid ratio is 50 barrels to 1MMcfg. The problem is being able to identify a reservoir around a well which has been producing from the early 1980s, but has never been offset because of poor well control and poor seismic data quality at that depth. The well in question has produced more than 50 BCF of gas and approximately 1.2 MMBO, and is still producing at a very steady rate. The operator wanted to know if the classification process could wade through the poor data quality and see the reservoir from which the well had been producing to calculate the depleted area and look for additional drilling locations.

The initial data quality of the seismic data, which was shot long after the well started producing is shown in Figure 2. Instantaneous and hydrocarbon-indicating attributes were created to be used in a multi-attribute classification analysis to help define the laminated-sand reservoir. The 3D seismic data areal coverage was approximately 72.5 km2. and there were 12 wells in the area for calibration, not including the key well in question. The attributes were calculated over the entire survey, from 1.0 to 4.0 seconds, which covers the zone of interest as well as some possible shallow pay zones. Many of the wells had been drilled after the 3D data was shot, and ranged from dry holes to wells which have produced more than 35BCFE since the early 2000s.

Figure 2. Seismic amplitude line through Tuscaloosa Sands at 5790 m. This key well has been producing for more than 35 years from 6 m of perforations.

A synthetic was created to tie the well in question to the seismic data. It was noted that the data was out-of-phase after tying to the Austin Chalk. The workflow was to rotate the data to zero-phase with U.S. polarity convention, up-sample the data from 4 ms to 2 ms, which allows for better statistical analysis of the attributes for classification purposes, and create the attributes from the parent PSTM volume. While reviewing the attributes, the appearance of a flat event seemed to be evident in the Attenuation data.

Figure 3. The appearance of a ‘flat spot’ in the Attenuation attribute.

Figure 3 shows what looks like a ‘flat spot’ against regional dip. The appearance of this flat event suggests that a combination of appropriate seismic attributes used in the classification process designed to delineate sands from shales, hydrocarbon indicators and general stratigraphy, may be able to define this reservoir. The eight attributes used were: Attenuation, Envelope Bands on Envelope Breaks, Envelope Bands on Phase Breaks, Envelope 2nd Derivative, Envelope Slope, Instantaneous Frequency, PSTM-Enh_180 rot, and Trace Envelope. A 10x10 matrix topology (100 neurons) was used in the SOM analysis to look for patterns in the data that would break out the reservoir in a 200 ms thick portion of the volume, a zone in which the Tuscaloosa sands occur in this area.

Figure 4. Time slice through the perforated interval from the SOM results showing a) areal extent of the reservoir and the appearance of a braided-channel system in the thinly laminated sands and b) only the key neural classification components shown from which the reservoir is better defined and extent can be easily measured. The yellow circle denotes the key well reservoir.

Figure 4a shows a time slice from the SOM results through the perforations with all the neural patterns turned on in 3D space as well as the well bores within the area of the 3D space. Figure 4b shows only the sand reservoir without all the background information. It can be noted that this time slice cuts regional dip in a thinly-laminated reservoir, so evidence of a braided stream system can readily be seen in the slice. Figure 4b shows the same time slice, but with only the key neural patterns turned on in the 2D Map matrix of neurons.

The result is that the reservoir for the key well ended up calculating out to 526 Hectares, which explains the long life and great production. It also seems to extend off the edge of the 3D space, which could add significantly to the reservoir extent.

Figure 5. Seismic amplitude dip line showing a synthetic tie that denotes locations of perforations, which is associated with a very weak peak event.

Case History 2 – Finding Hydrocarbons in Thinbed Environments Well Below Seismic Tuning

In this case, the goal was to find an extension of a reservoir tied to a well which had produced more than 450 MBO from a thin Frio (Tertiary Age) sand at 3289 m. The sand thickness was just a little over 2 m, well below seismic tuning (20 m) at that depth. Careful attention to synthetic creation showed that the well tied to a weak peak event. Figure 5 shows this weak amplitude and the tie to the key well. Again, the data was up-sampled from a 4 ms sample rate to a 2 ms sample rate for better statistics in the SOM classification process, then the attributes were calculated from the up-sampled PSTM-enhanced volume.

The reservoir was situated within a fault block, bounded to the southeast by a 125 m throw fault and along the northwest side by a 50 m throw fault. There were three wells in the southwest portion of the fault block which had poor production and were deemed to be mechanical failures. The key producer was about 4.5 km northeast of the three wells along strike within the fault block. Figure 6 shows an amplitude strike line that connects the marginal wells to the key well. The green horizon was mapped in the trough event that was consistent over the area. The black horizon is 17 ms below the green horizon and is located in the actual zone of perforations in the key producing well and is associated with a weak peak event.

A neural topology of 64 neurons (8x8) was used, along with the following eight attributes to help determine the thickness and areal extent: Envelope, Imaginary Part, Instantaneous Frequency, PSTM-Enh, Relative Acoustic Impedance, Thin Bed Indicator, Sweetness and Hilbert. Close examination of the SOM results (Figure 7) indicated the level associated with the weak peak event resembled some offshore bar development. Fairly flat reflectors below the event and indications of ‘drape’ over the event led to the conclusion that this sand was not a blanket sand, as the client perceived, but had finite limitations. Figure 7 shows the flattened time slice in the perforated zone through the event after the neural analysis. One can see the possibility of a tidal channel cut, which could compartmentalize the existing production. Also noted is the fact that the three wells which had been labelled as mechanical failures, were actually on very small areas of sand which indicate limited reservoir extent.

Figure 6. Amplitude strike line along fault block showing marginal wells on left and key producer on right. Mapped trough event is shown as well as a horizon 17 ms below the trough which is flattened and displayed in Figure 7.

Figure 7. Flattened time slice 17 ms below trough event after SOM analysis. A possible tidal channel cut through the bar can be interpreted. Note the three marginal wells to the southwest are on the fringes or separated from the main producing area.

Figure 8 is a SOM dip seismic section showing the discovery well after completion. Three m of sand was in the well bore and 2 m were perforated for an initial rate of 250 BOPD and 1.1 MMcfgpd. The SOM results identified these thin sands with light-blue-to-green neurons, with each neuron representing about 2-3 m thickness. This process has identified the thin beds well below the conventional tuning thickness of 20 m. It is estimated that there is another 2 MMBOE remaining in this reservoir.

Figure 8. SOM dip line showing tie of thin sand to bar.

Case History 3 – Using the Classification Process to Help With Interpreting Difficult Depositional Environments

There are many places around the world where the seismic data is hard to interpret because of multiple episodes of exposure, erosion, transgressive and regressive sequences and multi-period faulting. This case is in a portion of the Permian Basin of West Texas and Southeastern New Mexico. This is an area which has been structurally deformed from episodes of expansion and contraction as well as being exposed and buried over millions of years in the Mississippian through to the Silurian ages. There are multiple unconformable surfaces as well as turbidite and debris flows, carbonates and clastic deposition, so it is a challenge to interpret.

The 3D has both a PSTM stack from gathers and a high-resolution version of the PSTM. The workflow was to use the high-resolution volume and use a low-topology SOM classification of attributes which would help accentuate the stratigraphy. Figure 9a is an example of the PSTM from an initial volume produced from gathers and Figure 9b is the same line in the high-resolution version. The two horizons shown are Lower Mississippian in yellow and the Upper Devonian in green, both horizons were interpreted in the PSTM stack from gathers. One can see in the high-resolution volume that both horizons are definitely not following the same events and additional detail in the data is desired.

Figure 9. Seismic amplitude line in wiggle trace variable area format going through key producing well; a) PSTM from stack and b) high resolution PSTM. Upper horizon (yellow) is Lower Mississippian and lower horizon (green) is Upper Devonian picked from data in a).

The workflow here was to use multi-attribute classification with a low-topology (fewer neurons, so fewer patterns to interpret) unsupervised Self-Organized Map (SOM). The lower neural count will tend to combine the natural patterns in the data into a more ‘regional’ view and make it easier to interpret. A 4x4 neural matrix was used, so the result only had 16 patterns to interpret. The four attributes used were also picked for their ability to sort out stratigraphic events. These were: Instantaneous Phase, Instantaneous Frequency, and Normalized Amplitude.

Figure 10 is the result of the interpretation process in the SOM classification volume. Both the new interpreted horizons and the old horizons are shown to illustrate how much more accurate the classification process is at defining stratigraphic events. In this section, one can see karsting, debris flows and possibly some reefs.

Figure 10. Results of the classification using the high-resolution seismic data in a low-topology learning process. Both the new and old horizon interpretations are shown. Many interesting stratigraphic features are shown, including karsting, possible reefs and debris flows.


In this article, three different scenarios were given where the use of multi-attribute neural analysis of data can aid in solving some of the many issues geoscientists face when trying to interpret their data. The problems solved were using SOM classification to help define 1) reservoirs in deep, pressured, poor data quality areas, 2) thin-bedded reservoirs while exploring or developing fields and 3) classification of data to help interpret data in difficult stratigraphic environments. The classification process, or machine learning, is the next wave of new technology designed to analyze seismic data in ways that the human eye cannot.


The authors would like to thank Geophysical Insights for the research and development of the Paradise® AI workbench and the machine learning applications used in this paper.


Roden, R. and Chen, C., 2017, Interpretation of DHI Characteristics with machine learning. First Break, 35, 55-63.

Roden, R., Smith, T. and Sacrey, D., 2015, Geologic pattern recognition from seismic attributes: Principal component analysis and self-organizing maps. Interpretation, 3, SAE59-SAE83.

Roden, R., Smith, T., Santogrossi, P., Sacrey, D. and Jones, G., 2017, Seismic interpretation below tuning with multi-attribute analysis. The Leading Edge, 36, 330-339.

Santogrossi, P., 2017, Technology reveals Eagle Ford insights. American Oil & Gas Reporter, January.

Smith, T. and Taner, M.T., 2010, Natural clusters in multi-attribute seismics found with self-organizing maps. Extended Abstracts, Robinson-Treitel Spring Symposium by GSH/SEG, March 10-11, 2010, Houston, Tx.