Machine Learning – New Discoveries & Reservoir Optimization

Deborah Sacrey, owner of Auburn Energy, presented at the the AAPG Deep Learning TIG for a free lunch and learn on Wednesday, 5 May 2021 at 12–1 p.m. (CT). 
Deborah Sacrey | Principal Auburn Energy
Deborah Sacrey
Owner – Auburn Energy

Deborah is a geologist/geophysicist with 44 years of oil and gas exploration experience in Texas, Louisiana Gulf Coast and Mid-Continent areas of the US. She received her degree in Geology from the University of Oklahoma in 1976 and immediately started working for Gulf Oil in their Oklahoma City offices.

She started her own company, Auburn Energy, in 1990 and built her first geophysical workstation using Kingdom software in 1996. She helped SMT/IHS for 18 years in developing and testing the Kingdom Software. She specializes in 2D and 3D interpretation for clients in the US and internationally. For the past nine 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 public, guided by Dr. Tom Smith, founder of SMT. She has become an expert in the use of Paradise software and has seven discoveries for clients using multi-attribute neural analysis.

Deborah has been 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 also Past President of the Gulf Coast Association of Geological Societies and just ended a term as one of the GCAGS representatives on the AAPG Advisory Council. Deborah is also a DPA Certified Petroleum Geologist #4014 and DPA Certified Petroleum Geophysicist #2. She belongs to AAPG, SIPES, Houston Geological Society, South Texas Geological Society and the Oklahoma City Geological Society (OCGS).



Susan Nash: 

Hello everyone. I’m Susan Nash AAPG. Thrilled to be here today with our very first lunch and learn with our deep learning technical interest group and the topic today is machine learning: new discoveries in reservoir optimization. Deborah Sacrey of Auburn Energy is here and she has a more specific title for the paper but it’s really exciting and new discoveries and new possibilities. So I’d like to turn the floor over to Patrick Ing who will tell us a little bit about the technical interest group and introduce our speaker.

Patrick Ing:

Thank you Susan. Welcome everyone to this [crosstalk] or supper depending on where you are and deep learning group in AAPG is really a technical interest group. I’ll give you the link, you can contribute and keep the conversation going after this. Over the last 12 months at least, we’ve seen an emerging pattern of using ensemble techniques like random forest or custom techniques like support vector machine in automating lithology identification and also on the seismic side, we have seen the use of deep convolutional neural network for map interpretation.

Now what the challenge remains is when we work across the scales, mixing seismic and the [inaudible] resolution. So today, we’re in for a treat. Our speaker Deborah Sacrey is an experienced geologist and geophysicist. You can think of both the left and the right brain. In addition, she has hands on experience working software as well as actual field project experience. So therefore, today she’s going to dish up [inaudible] artificial nuance and show us how we can use it perhaps to uncover overlooked or new opportunities. So without further ado, I will turn control to Deborah.

Susan Nash:  

Welcome Deborah and just quickly everyone put your questions in the chat box and we’ll answer them at the end. Welcome Deborah.

Deborah Sacrey:  

Thank you Susan. I’m sharing my screen now can you see it okay?

Patrick Ing:  


Deborah Sacrey: 

Okay and I probably will go dark on my camera, just so I won’t be a distraction to myself. Good day everyone. I would say morning and afternoon and evening because we’ve got people from over the place but what I’d like to discuss today is some work that I’ve done, case histories I’ve done on two different reservoir types, both unconventional and conventional and show how machine learning can help define sweet spots, help you calculate reserves and a lot of other technical things that are a lot of fun.

I’m going to start out with, let me get this going. Okay. I’m using some software called Paradise. Now, Paradise is a company that was started by Tom Smith, Dr. Tom Smith back in 2008 after he sold Kingdom and he was still not ready to retire so he started playing around with neural networks along with Terry Turner and the application of learning classification of neural networks using self-organized maps in multiple seismic attributes. So what we’re doing is we’re looking at multiple seismic attributes at one time and we’re looking at the variations in those attributes because they all contain information.

The premise behind paradise is instead of a wavelet processing or classification, say like Stratomagic used to do, we’re looking at everything on a sample basis. So sample statistics and if you have a wavelet that’s 20 or 30 milliseconds and you have one millisecond sample rate, then you’re parsing that data and looking at it statistically 20 or 30 times what you probably would do when you’re looking at a wavelet and mapping zero crossings or peaks or troughs. So this generates a lot of information but it also lets you see a lot of fine detail in the sub surface and one reason why we use multiple attributes like I showed earlier is that along each sample, each different attribute, the wavelet of that attribute carries a little bit different information in it and we put all of this stuff in a cloud and then have the neurons look for the natural clusters of data in that cloud and what happens because each of the data points have an XY and Z to them, when it comes out of the cloud as a cluster, it puts itself back in a volume very easy to interpret.

So what you end up seeing is you end up seeing voxel and I call them chicklets because they remind me of the candy gum we used to eat, and the voxel is going to be based on the sample interval that you’re using and the bin size of the data. Now, 99% of my work is in 3D but this process also works in 2D data if you’re in remote areas where you don’t have a lot of 3D information. Now, in conventional work, in the wavelet, we have what we call a tuning thickness and that’s based on frequency and depth and the quality of your seismic and in the sample world, when we’re doing classification of samples, we really don’t have that. Since we’re looking at sample rate, we’re looking at the vertical resolution of the sample statistics is based on the interval velocity of the rock itself from which the sample is taken. So there’s no interpolation and it’s based on what it’s seeing regardless of depth, regardless of frequency. So I’ve been able to find seven foot thick sands at 11,000 feet in the gulf coast. I’ve been able to see 18 foot porosity intervals in carbonates at 2100 feet. So it’s based on the velocity of the rock at the sample.

Now with that, I’ll get off my soap box about Paradise and get onto the work I’ve been doing. This work was actually done several years ago, TGS approached us to kind of a proof of concept, and wanted to see how well this classification process would work in an unconventional reservoir in Oklahoma. Most specifically, we’re looking at the Meramec formation in central Oklahoma across Blaine County and Kingfisher County. So they gave us a 195 square miles of data and the project objectives were to see if we could discriminate production in the formation and tell why some wells were better than others and understand the accuracy in the neural classification results.

So part of the assumptions and challenges to this was the production is not always related to only geological changes and because there was a combination of long laterals and straight holes, we decided we were only going to look at the straight hole data because of the variance that can happen between fracking and lateral length and things like that and to some degree the operators. So the only thing that’s static and good in here is going to be what happened with the straight hole data. That porosity and permeability could not be calculated from the log curves provided in order to calibrate the wells. Because we were using straight holes, most of the wells were drilled in the 80s and 90s. My newest well in there was a 2003 I think. So the log curves left a lot to be desired.

There was difficulty in isolating specific production in all the wells through the perforated zone because of the age of the wells and because of the nature of the rock itself, and some wells were shut in at times and not so they were hoping that we could find being able to isolate specific production in perforated zones. Again, the decision was made to use only straight holes because of variables in completions in the lateral wells.

So the first thing they did was add, this is 195 square miles. You can see the live trace outline right here and I know the other writing is a little bit small but they gave me 35 wells and they held five wells back as blind wells to see how well I could predict what was going on. So what I’ve done I’ve taken the cumulative information for production and the really poor wells are in green. The medium class wells and those are wells that have produced 50,000 barrels or greater, are in pink and then the very best well out of the 35 wells had accumulated 240 barrels of oil and 2.23 BCF of gas. This was my star child that I really wanted to look at and see if I could see what was going on in this well as opposed to all the other wells in this 195 square miles.

So the first thing I did is I mapped the actual top of the Meramec formation which is Mississippian in age. Again, this is in central Oklahoma in Blaine and Kingfisher counties and this is the structure map. Now what you have, is you’re getting deeper as you go to the southwest because you’re going into the fringes of the Anadarko Basin which is a very deep basin. My good well is right here the Effie Casady well. I have a North South cross section that I’ll be focusing upon and then I also built an East West cross section through the medium wells and poor wells.

So the data quality is excellent TGS did a wonderful job and I have to thank them again for allowing me to do this work but what I want to show here is the top of the, this is Pennsylvania the big line which in some areas they call the Osage, the Mississippian starts right about in here in this middle section, the top of the Meramec is this hot pink horizon right here and then you get into the base of the Woodford which is this, the Woodford is this trough and if you look, my color bar, I keep my troughs in black and my peaks in orange. I call it my Halloween color bar. I’ve used it for 30 years but this black trough right here is basically the Woodford section itself and the Woodford ends up being the source rock for the Meramec production.

So basically we’re looking at this interval between the pink and this yellow which is the base of the Woodford. That’s my horizon limits. One thing I want you to notice is that as you start on the North side of the survey and look at the South side, you can see an expanding section. So the Meramec is getting thicker and the Woodford is getting thicker. So that should be one clue as to why my really good well down here might have produced a little bit more than the wells to the North which were generally poorer wells.

Now, one of the things I go into is I look at principal component analysis. I generate about 17 or 18 attributes, all instantaneous attributes at first. In this case I did not have any ABO data, I didn’t have any angle stacks or offset stacks. I only had a PST in volumes and then they gave me a series of inversion loggings. I probably won’t talk much about the inversion because they didn’t work well but my opinion about inversion is that it’s good when you’ve got a lot of well control but it’s a modeling process in and of itself so when you get very far away from your well control, the model starts to fall apart as opposed to classification or SOM which is one neuron equals the same rock properties wherever you see it in the data.

I went through principal component analysis and looked at all 16 of my instantaneous attributes and the first item vector, it started breaking out those attributes which were more pertinent to successful classification in the volume than some of the other attributes. So right off the bat I see five attributes that contained about 70% of the data that all 16 would have. In looking at the item value numbers, you can see a lot of variance in the data set. The way Paradise works, it does this principal component analysis on every inline in the data set and the green line represents the median for the whole data set but you can pick any one inline and look at the values and the appropriate attributes that show up in the top of the item vector anywhere you want or do a group across a field.

So I used a recipe looking at first the top attributes in the first two item vectors. So in this case I’ve got attenuation, envelope, Hilbert, instantaneous frequency, instantaneous phase, normalized amplitude, relative acoustic impedance, sweetness, and thin bed. What I notice right off the bat is, here’s my really good well, this is the 240,000 barrel well right here, is unlike a lot of the other wells in the area where they had just perforated the whole Meramec section or the majority of the Meramec section, the operator on this well perforated a bulk of it and then did spot perforations in two other areas. I got to looking at the area down here that had unique neurons or classification clusters that were not apparent at the base of any other well or very many wells in this section. So here’s neuron number 71, which is kind of brownish in color and neuron number 72 which is yellow and you can see the yellow and the brown over here occur just mainly around the Effie Casady well and a couple of other areas but not very consistent in the rest of the section.

So what I’m able to do is I am able to go in and kind of squeeze down and look at these and when I pulled up the appropriate log curves for the Effie Casady well, I noticed that the two sets of perforations right here and right here, corresponded to better resistivity in that well and of course sometimes a better resistivity in unconventional play in the rock that’s more brittle and can be fracked more easily or it might contain a sandier section with the higher carbon, a higher TOC. So I was especially interested in this lower zone again, where I had neurons number 71 and 72 and it had the highest resistivity of anything else in the well in the Meramec section.

So I can isolate those two neurons, I can turn everything else off and isolate just those two neurons and where they occur in this whole 195 square mile area. We can do that because we can take it, the upper horizon and the lower horizon and we can squeeze it down to eliminate spurious information and just concentrate on our zone of interest down here which are these two lower sections of perforations, what I call the sculpted interval. When you do look at that, you’ll start noticing that the Effie Casady is right here, this area were my medium wells and everything else, if we go back to the production map, everything else was in the poor category. So that tells me right off the bat that these two neurons are probably key to not only porosity and permeability but also to production in this whole area for the Meramec.

Now, one thing we can do is we can take the voxels because we know that there is a sample height, in this case one millisecond and the area, the bin spacing of the 3D and we can turn those voxels into actual little volumes and this is a process that we go through to create Geobodies. Once you have your Geobodies created, then if you know very specific information, you can turn around and turn those Geobodies and look at them in terms of reserves. So that’s what I did in this process. This is the Geobody that corresponds to that lower zone of perforations and the assumption I’m making here is that is where the predominant amount of hydrocarbons came from because it’s the highest resistivity, it was a very specific zone that they perforated and it’s abundant around the Effie Casady well.

So if you know, I have the sample counts, interior and exterior, if you know the velocity of the rock, you know the net to gross ratio of your zone of interest in that Geobody, you know your porosity and you have your water saturation, then you can calculate how many hydrocarbon core volume in cubic feet. Now we can do this in acre feet as well, would be attached to that particular Geobody. What I did is I have a buddy who worked for a larger independent who was in charge of the Meramec group in Oklahoma and I just asked him I was like, “What are you engineers using for numbers in this area for their reserve instruments?” He came back with 14,000 feet per second for velocity, he said generally they’re using a net to gross ratio of 60%, we’re looking at six percent porosity for some of the better wells and we’re looking at a 40% water saturation.

So when you take all those numbers and he also told me that they’re getting roughly about 250 barrels of oil per acre foot, then I can go in and I can look at that number and compare it to the actual produced number in the well. So I did that, divided it by 43,560 and I got 2725 acre/feet for this green Geobody and I multiplied it by the 225, I said 250 earlier but it’s 225 BOE/acre foot and I came up with a number of 613,125 BOE. So what I did is I took the oil production and the gas production in the Effie Casady and I converted the gas to a barrel of oil equivalent and what the well has actually produced is 611,685 as opposed to the calculated amount. Now this well is still producing, it hasn’t been plugged and it was drilled in 1980. So it’s been producing 50 years which is a long time, 40 years, 41 years and still has some production left in it. Of course you never get everything out of the rock but if you look at the number of actual compared to what I calculated, I’m only basically one percent off in error. So that’s pretty good, I would challenge most engineers to get that close when it comes to estimating reserves.

So I came back and had a meeting with TGS and showed them my results and they said well, I mean you could have gotten lucky by estimating the reserves for the Effie Casady. What about the group up here that were the medium group and it turns out, here’s the tree or four wells that are actually in the medium range, their cumulative in barrels of oil equivalent values. So the cumulative total for those wells is 581,883 barrels of oil equivalent. So I went back in and I looked at them and three of the four wells actually penetrated the 71 neuron but this last well over here didn’t perforate in that neuron, it perforated in a zone up here just above the Meramec. If you look at the blowup of the neural information, you can see that the number 72, the brown neuron right here, they perforated in here but this well did not and they didn’t have any of the yellow number 71 in here like the Effie Casady did which probably gave it so much better production.

In looking at them, here’s the 72 neuron here in this well and here in this well, and this well had it but it didn’t perforate it. What’s interesting is the zone of perforations in the one well and the three zones above the Meramec also perforated in 71 and 72. So the 71 and 72 neurons are indicators of better resistivity and higher carbons. Totally unrelated to what we’re seeing down here in a different complete section but the same neural information.

So I went back and I looked at the Geobody associated with that neuron and this well right here did not produce from that neuron in the zone of interest but this well right here, the Clydena did. So I took out the production from this well, I took the production from these three wells and this well and ended up with 481,681 BOE equivalent as opposed to the 475 that they’d actually dug and then added the Clydena to it and so the bottom line here is the four wells which did produce from that Geobody, was less than a 2% error to what the actual production was and what was calculated. So again it looks a little dendritic but if you’re looking for a sandy zone in an unconventional reservoir or you’re looking for porosity streaks, you would expect it to look something like this and not just a big clean reservoir.

So after that TGS was happy with the work and I think that they actually had Paradise up in the cloud along with their data. So if you’re a data subscriber to TGS Data, you have access to be able to use Paradise to do your work with their seismic data. Now a follow up on that, Paradise has the AASPI Consortia Software in it and that’s Kurt Marfurt out of the University of Oklahoma. We license that software to run geometric volumes in and so this is an example of a time slice roughly at the base of the Woodford showing little channels and stuff in the Woodford shale itself that are coming through in the similarity volume and I can also look at fractures. This is 17 milliseconds above the base of Woodford roughly about the zone of the lower perforations in the Effie Casady well.

So I can definitely see some fracture trends and I think there are some busts in processing right in here that TGS was not aware of but this software is pattern recognition. So if there’s a pattern in the data, it’s going to pick up on it and the unintended consequences sometimes are picking up acquisition footprint, bad seams in merges, processing busts, there are all sorts of patterns up there coherent and incoherent patterns that you can pick up with neurons other than just geology but there are some definite fracture trends in here that you can see going on throughout the Meramec. I ran a SOM using two curvature volumes and two similarity volumes so that I could turn on and turn off clusters and it turns out that the vast majority of fractures and lineaments in here can be seen just with four neurons. So I thought that was pretty interesting.

So the conclusions from the study is that SOM can be very effective at finding sweet spots even in unconventional reservoirs. Now I find them all the time in conventional reservoirs and we’re getting ready to get into that. Especially where there are changes in deposition, siltstones, calcareous sands, et cetera and I’d even used this process in West Texas in the bone springs and the Woodbine, not the Woodbine, Wolfcamp to find little thin calcareous sand channels and stuff even in those environments. Geobodies can and are related to finding porosity streaks and can be back calculated to production for use in reserve estimates and it’s very accurate. It’s a good way of going forward to estimate new sweet spots which have not been drilled.

The key to all of this is understanding that the depositional environments, I mean applying the geology to what you’re seeing in the seismic data, tying to wells with synthetics because here, unlike a wavelet where you can kind of be loosey goosey with your synthetic, you’re tying the very specific neurons that are sample based. So you need to know with good synthetics what you’re trying to and then also understanding the function of attributes that you’re using in your analysis. Some of them are good for[inaudible] some of them are good for porosity, some of them are good for hotter carbon indicators. So it’s the combination of attributes that you use in your, what I call recipe, that are going to get you further down the road in understanding what’s going on in the subsurface.

So next, I’m looking at exploring in a conventional play in East Texas and the premise behind this study which was more recent than the Meramec study is I had a client who operates a field and actually ends up owning a lot of the minerals in a very good East Texas field which was discovered back in the 90s and there have been a lot of good wells, a lot of production out of this field. This is another proof of concept for them to see if unsupervised classification on a sample scale could help identify reservoir rock in Cretaceous sands below 13,500 feet. So in the Meramec world, we were dealing with rocks around the 8500 to 9000 foot range and now we’re getting deep, we’re in the 13,500 to actually 14,300 range across this 3D and to see if there were any remaining locations left to be drilled. They thought they’d gotten it all but there’s always hope that there were one or two spots that they left behind. Again, they gave me a 3D survey, PSTM only, I did not have gathers, I did not have any outstanding AVO volumes or angle stacks, they gave me wells, production, tops and digital logs.

So this first map you’re going to see is a map on the key horizon just above the productive Cretaceous sand and this map is based on the unconformable surface below, and I can tell you it’s below the Austin Chalk. The base of the Austin Chalk is an unconformable surface and this Cretaceous sand is coming up and butting against the unconformity. I have two cross sections I’ve built in here again. A West to East cross section and a South to North cross section. Now in this case, the wells with red squares around them are those wells in which I’ve created synthetics and there is a very good velocity variance across this 3D as you go from South to North where the section is thickening again and you’re getting deeper and from West to East even and there’s a big fault over here that you can cross over and there’s a big velocity change across that fault. I’ve turned off all the shallow productions because there is shallow production in here. So everything that’s shown is 12,000 feet deep or greater.

Now the South to North arbitrary line is what you’re looking at right here and this is the unconformable surface that we’re going through in this area and the sands, it’s probably a little bit harder to see here but generally what is happening is these Cretaceous sands are coming up unconformably and hitting the horizon and they keep coming up. So sometimes across the area you may see five or six different sands and they’re not the same. They have different rock qualities but the production in here is from very poor production to some medium good production, this is a 24 BCF well that made over almost 1.3 million barrels of oil and you can see the perforations in here.

Now, the West East, the sand is a little bit harder to map. It’s a little bit more spurious in its occurrence but the premise is still that this is an angular unconformity and they’re coming up in different zones and butting up against it. This actually goes through my very best well in here which made 33 BCF and almost 1.8 million barrels of oil. So there’s a 26 BCF well and then you get over here to some poor wells that are even structurally a dip from good wells which you’ll see the difference in the rock quality when we get into the SOM.

Now I did the same process here. I took all of my instantaneous attributes that I created from the PSTM volume and I went in to look at principal component analysis. So right off the bat, the top five in the first icon vector happen to be relative acoustic impedance which to me is a porosity indicator. Envelope, Hilbert, envelope second derivative, and then sweetness which I use consistently in the gulf coast as a hydrocarbon indicator. So we’re getting the top five group right here which is again, over 60% of the relevant information from all the rest of the attributes used in the analysis.

This is the South to North after processing and here’s my recipe. So I’m using thin bed because some of these sands are very thin sands. Relative acoustic impedance, envelope, attenuation I threw attenuation in there because there’s a lot of gas associated with this and I wanted to see the effects of the gas in the SOM. So right off the bat, I’ve started looking at the perforations and identifying those neurons which are repeated over and over again in some of the better wells and here you can see the combination of number 37, 47, and 48. Now, these are just clusters. I haven’t ascribed any rock properties specifically to them. I’m just looking at the way that they accumulate or assimilate next to the production in the wells and I have the production for each well in here. So in number 37 which is this darker blue, number 47, 48 which are these kind of teal colored neurons right here. Again, 37, 38 which are blue.

So there are some neurons that in the better wells, 12 BCF, 8 BCF, you don’t see them so much in the 1 BCF but again, in the seven, that keep showing up over and over again and then when I look at my West East, here again, I’ve got some wells that are poor that are structurally higher than some wells that are very good. Now for the first time, you start seeing this yellow neuron come in and here is the very best well in the field, the 33 BCF well again. This well, after talking to the operator because I was concerned that I have all this yellow and he said they had a 300 foot thick sand, it was the most beautiful sand they’d ever seen, permeability and porosity just gorgeous. It was just down thrown across the fault with the water level in it. So the fact that this yellow neuron is associated with the best well and one of the poorer wells, has only to do with structure. It has nothing to do with the quality of the neuron itself which I believe in here without having angle stacks is not showing me fluid level as much as it’s showing rock quality level.

Now what I’ve done and this is something that’s fairly recent in paradise is to be able to build cross sections and actually extract some information or neural information along the cross section. So now we can start, and again it’s valid only if you have really good synthetics and you know what you’re tied into. So now in the South to North, I have kind of a grading system for the wells, poor being zero to five BCF, fair being five to 10, good being 10 to 20, great and then excellent is anything greater than 25 BCF. I can start associating very specific neurons with wells in their category. 55 and 37 with the well that made 24 BCF. Six, 17, 18, 27, and 48 with a well that was in the medium or good range. So I can start quantifying the neurons with the production in these wells and I did this on the South to North cross section and again my zone of perforation is right here and in my West East and again here is my really good well and the well that was down thrown, I don’t have a log on that well, down thrown and he’s saying that it’s a really thick sand, I’m assuming that 73 is covering that sand but only perforated in here because they hit the water level.

So I can start to get a feel really quickly which neurons are good and which ones are medium and which ones are associated with poor production and what I’ve done here is I have much like in the Meramec, is I’ve pulled up all the neurons within that zone of interest and I think I went from the unconformity down 30 or 40 milliseconds because the bulk of the production is within 30 or 40 milliseconds of the unconformity surface itself. What you start to see with the poor, the fair, the good, the great, and the excellent is you start to see some of the better neurons associated with the East side of the field and some of the poorer neurons with the West side of the field and they breakout interestingly enough, in clusters that are pretty much at polar opposites in the whole map. So what happens is, clusters that are close together have very similar properties but just enough to distinguish themselves in separate clusters. So this group is very similar to each other but in four separate clusters. So this is the group of poor neurons associated with poor production and these are the neurons that are associated with the good, the better production and excellent production.

I can look at each layer independently and then look at the associated attributes that go with that layer and their contribution to that cluster. So in this case, the yellow layer which is with the best well and here’s my best well right here and you can see the cross section lines coming through, is Hilbert relative acoustic impedance which again, I’m using as porosity indicator, envelope and sweetness which can be hydrocarbon indicators and then everything else is not contributing that much. So here is neuron number 73, here’s neuron number 55, and again, the associated neuron independence that goes with the attributes there and then finally here’s neuron number 37 and you see attenuation starting to come in along with relative acoustic impedance. So you’re starting to see some of the gas effect coming in, in this neuron and here’s the best well.

Now, similar to what I did with the Meramec is in Paradise we have the ability to look at data based on the probability of those data points being in the center of the cluster. So each cluster has a scattering of data pints that make up that cluster. What I’ve discovered over the years is those data points which are on a peripheral edges of the cluster yet still being a part of that cluster are the most anomalous to that cluster. In the gulf coast, if I have had angle stacks or offset stacks or I have gradient intercept, I have AVO volumes that I’m using in my analysis, I have found that the AVO effects are most concentrated in these outside clusters. Excuse me one second I’m going to get a drink.

So what I want to do is I want to turn off all the data points of the clusters and only look at the outer 10% of information which is my most anomalous information to this area. When I do that, you see a lot of the data goes away. Now, this 3D was shot well after the field had been discovered and was being developed. So let me go back to, excuse me. Let me go back to, here’s all my clusters with all the data points turned on. Now, when I only look at the most anomalous 10%, what I think I’m seeing here is I’m seeing signs of depletion in the field. There’s nothing anomalous about these areas anymore because they’re all depleted. However, there are four zones around the edges of the field which have not been depleted. Now I killed this one because it has a dry hole in it and if you peel off the layers, it has a lot of the bad neurons in it but this little area right here, this well right here, deviated well is, the base of the well is right here. So that production sign is not pertinent to this zone and two zones that are down here that have substantial size to them and you can see the green and the yellow and the blue that’s associated with what we saw in the best well.

Now, this area right here is downthrow or lower so it may carry a little higher risk with it but for right now, these are my four zones that I’m seeing that are left after depletion. So I created Geobodies with them and I want to go in and look at the Geobody information now but before I do that, I want to prove to myself that what I think is a good neuron for production in here is actually so.

So here’s one of them I’m looking at and just off the cuff, if you look at the ariel extent of the Geobody and the neurons in it, you’re coming up with numbers that if you were assuming a velocity of 14,000 feet per second, and a net to gross ratio of 80% in here and a 25% water saturation and a 30% porosity, then you can come up with this area right here which is dry holes on one side and oil producing well on the other but it’s not depleted, then you can put 20 BCF in 1.1 million barrels of oil in that blob.

Here, I’ve taken 15% of the yellow because I can’t run a Geobody on low probability in formation but I’m still coming up with 13 and a half BCF and 727,000 barrels of oil and the two areas to the North are a little bit smaller, they’re in the good category, 8.6 BCF and 460 plus thousand barrels of oil and finally 5.5 and 290,000 barrels of oil. Now that’s my estimate based on the fact that I think that these Geobodies are related to neurons which are related to reservoir quality. These are 6.2 million dollar wells so they’re not for the faint of heart to drill for my client but he said okay that’s good and well but how do you prove that those are good hydrocarbon neurons?

So one of the ways we have of going about looking at it is to look at bi-variant statistics and we’re going to go back to the cross sections now and the neurons that were associated with the wells. So I can put wells in and I can till up the zone of interest I wanted to use and I can pull up the associated SOM classification volume that I ran and then I can give it some very specific petrophysical cutoff information. In this case, I’m looking for gamma values less than 60, I’m looking at 10% porosity or greater, I’m looking six ohms or greater of resistivity when it comes to what determines what’s wet and what’s not. Then in the kind of square table, I can actually, if I had multiple runs with multiple topologies, this happened to be an eight by eight, then I could go in and it would tell me which topology it thought was the best for the analysis.

What happens is, and when you give it the cutoffs and you give it the zone of interest to look at, it’s going to look at all the neurons in that zone of interest and it’s going to determine which ones are really related to reservoir quality or reservoir and which ones are not. So in this example, this good well right here which produced 12.6 BCF and 175,000 barrels of oil, it’s looking at neuron number 38 and neuron number 41 which happen to be the two neurons associated with the production and said yes, those are reservoir rocks. Now neuron 63 which is up in here, has some reservoir in it but it’s mainly non reservoir and then the kind of square will do a known hypothesis theory and will tell me it’s rejected or accepted. In this case the known hypothesis is rejected which means that the alternate hypothesis is that there is a relationship between net reservoir and lithological contrasts on variables.

For most people in the past few years, they have looked at the SOM work that we’ve been doing or I’ve been doing and to them it’s a black box because they keep thinking, they keep trying to go back to rock properties. Well here is where you can take the black box out of it and associate specific clusters of information with reservoir quality rock. So that’s a case for the good well. Here is a poor well which only made 165 million cubic feet of gas and 2800 barrels of oil. Well I looked at the perforations across that reservoir, used the same basic cutoffs and here I’m looking for greater than eight ohms of resistivity and eight percent porosity and it’s telling me that it’s got some reservoir in those two neurons but it’s telling me most of it is not reservoir and based on the poor production and the really tight sands or ratty shaley sands in here, I can see why I don’t have a lot of good reservoir and why the well-produced poorly. Now it’s still saying that there is a relationship between the net reservoir and lithological SOM contrast but it’s just telling me I have more non reservoir than I have reservoir and again, it’s held up by the poor production of the well.

Now for the final well that I looked at was my best well in number 73, 35, and 55 and in here, it’s telling me that 55 and 73 are definitely reservoirs but there’s a little bit of non reservoir 73 and if you look over here at the perforations, you see this area that’s a little bit wetter and a little bit shaley-er so it’s picking up on just that piece of reservoir which is non reservoir and letting me know that there’s some non reservoir in there. Again, it’s rejected the known hypothesis and telling me that there is a relationship between net reservoir and lithological contrast SOM variables. So here’s a way I’ve been able to go in and evaluate a field and look for pieces that were left over and verify that the Geobodies left in these areas can contain if one was to go in there and drill it, an amount all four areas is equal to somewhere over 55 BCF and close to four million barrels of reserves left in this area. You just have to want to get up and spend 24 million dollars coming in here and drilling these areas.

So the summary and conclusion on this is that I can go in here using multiple seismic attributes and show patterns in the Earth that you can relate to reservoirs when calibrating to wells, that the use of Geobodies again, can predict accurate volumes of potential hydrocarbons when you have the correct input data and that here, the use of low probability can help identify stranded reserves in a field and I’ve done this in other fields I just don’t have the show rights to be able to show you guys and the application of statistical petrophysical methods can verify and reduce risk in identifying reservoir-grade rock in the potential stranded areas. Now this workflow is not one and done, I don’t stop at one recipe. I’m constantly playing with it and looking at different topologies, the number of clusters I’m using to identify but it is a very good statistical methodology to go in and look for stranded reserves.

So that kind of is the end of the unconventional and the conventional case. The last thing I want to show is some of our latest work that we’re doing in the SOM work and that has to do, and I want to thank TGS and my Texas client and a big thank you to Carrie Laudon of Geophysical Insights for guiding me through the statistical analysis process because when I did this last year on this East Texas field, I was clueless as to how the bi-variant statistics process worked.

This last stuff is some stuff I’ve been doing in northern Columbia and we’re getting into convolutional neural networks now and associated with SOM and working with the AASPI software that we have in Paradise to look at how we can go through and eliminate the need for manual picking of falls which is the most dreaded thing a Geoscientist has to do when they get a big survey. This survey happens to be about 400 square kilometers in northwestern Columbia and you can see it’s very complicated when it comes to the fault regime. Now what we’ve done is we’ve taken a two millisecond sample volume and we have created a neural network of fault basically faults in here but we’ve upsampled the formula second to run it through our convolutional neural network. This gets rid of unwanted noise and possible false positives. Then we can go into the AASPI software and take the CNN fault volume and skeletonize it down to just the basics, getting rid of false positives again noise and getting it down to what looks like a valid fault interpretation volume in this area.

So I bring this into paradise as an attribute volume and I can pull it up in the 3D viewer and look at what I believe would be the fault planes as opposed to the fault background and some of these other areas, the white areas. Then I want to use it in SOM in the classification. So I will use it in combination with instantaneous attributes and get something that looks like this. So now I can see my fault planes and the faults clearly associate themselves in one corner of the 2D map that we have in 3D viewer and I can go in and then turn on the instantaneous attributes that I believe would be associated with the reservoir rock and I’m going to focus on this one well right here because this is a well that was recently drilled by the client and came on pretty well for gas but then watered out. So they’re wanting to understand an explanation of why it might have watered out and I can see stratigraphy along with the fault web itself.

So here’s a closeup of that well and it’s very close to the fault and what I think happened is, I can see the different layers of the perforations, they perforated three different zones in here and it was the upper zone that watered out at the very, very end and not only do I think that they probably coned water up to the fault plane, but I think that the upper reservoir which finally depleted, either depleted on test or it was compartmentalized to where it wasn’t very big in association with the water they were getting up the fault. Now here’s the reservoir neurons and the fault neurons, and here’s another look at it. So they can come updip and get past this compartmentalization right here but then the question is, does this fault go all the way through and this is an upper structure right here. So maybe the gas has migrated, you’ve got some low saturation gas in here which could be contributing to the watering out and maybe the good gas is all migrated up around this fault into this upper structure right here. So here’s my reservoir neurons, here’s my fault neurons. If i had this in the 3D viewer right now I could rotate this around and you would be able to see how beautifully these fault planes are coming out with that skeletonized volume in SOM.

So with that guys, I say that’s the end of my presentation. If Susan is ready to take questions and send questions my way I’ll do my best to be able to answer them.

Susan Nash:

Great thank you. So why don’t we take a look at some of the questions. Let’s see I didn’t take, so

Deborah Sacrey: 

I’m going to get out of this Susan real quick so that I can go back to any one slide if people kept notice on the numbers of slides that they have a question to a particular number of slide.

Susan Nash: 

Okay. Great and I don’t know about a PDF of the slide presentation being available but you will have the recording available and here’s a question, is the vertical window of neurons completely random or can you give it a certain minimum thickness?

Deborah Sacrey: 

No, in fact a lot of times what I do you can go above or below a horizon in milliseconds if you want or depth, all this is generated in depth volumes as well as time volume but a lot of times what I will do is I’ll do a window between two specific horizons and basically the only interpretation I do in the conventional models anymore is major flooding surfaces because I use those as my horizon limiters but no you can do it by time, you can do it by horizon, you can do it just about any way you want.

Susan Nash:

Great. Here’s a question from James Hill, will this technique work on 100% 2D data?

Deborah Sacrey:

Absolutely. Now, getting into some of the geometric volumes, you know that the geometric volumes are multi-traced calculations. So generating a similarity or a coherency volume or curvature volume is always a little bit iffy when you have 2D data because you’re looking at one line of trace instead of multi-traces. That’s not to say that you can’t do it but certainly the instantaneous volumes, I’ve got a prospect in South Texas right now that I only have five 2D lines on. So we’re hoping to get enough interest to shoot a 3D image before we drill a well.

Susan Nash:    

Another question from Kimberly Wagner. What kind of well density is needed in order to get reasonable or reliable results with this sort of analysis?

Deborah Sacrey:   

Well you can do this without wells. I have down offshore West Africa for a client without any well control at all. You run a little bit higher risk in doing that but if you can start seeing flat spots, if you can see distributary sand systems, you see patterns that are geological in nature, you don’t have to have well control at all. You have to have good concept and geology and understanding the attributes that you’re using to be able to pull out information. You definitely can see flat spots if you’re using fluid type attributes AVO volumes.

Susan Nash: 

Great. So Valentina Bartinova is asking are SOM and PCA considered deep learning?

Deborah Sacrey:   

In my mind they’re deep learning. They’ve been around for a long time, people might think that the convolutional neural networks are probably deeper learning than SOM but I’ve been using this process for about 10 years now. I’ve probably worked on between 150 and 200 different surveys around the world and the classification process for understanding the subsurface stratigraphy and what mother nature has given us to work with is so superior to anything else you can do in the conventional seismic world, it just, I can’t ever go back to conventional wavelet data again. Every time I look at wavelet data, my mind is trying to imagine the subtleties of the clusters that are going on in the background.

Susan Nash: 

That’s interesting. Patrick Ing comments that think of them as tools in a toolbox. PCA is one of the tools in data science. SOM is one of the earlier developments in clustering algorithms. So I don’t know if you have a thought about that? Okay. Has anyone used this type of analysis for Geosteering horizontal wells? This is from David Grumbo.

Deborah Sacrey: 

So David, we have several clients that are in the unconventional world and I can tell you that based on the detail they can see especially in Eagleford when we can zone in on the sweet spot on the Eagleford, they definitely are using it in Geosteering.

Susan Nash:

Interesting. So Bob Kirkland asks, is Kingdom the best software for doing all of this?

Deborah Sacrey:  

Well now Kingdom is not part of Paradise. I happen to use Kingdom, I’m an independent, Kingdom is a lot cheaper than Patrel for me out here in the country but the only thing I do in Kingdom is I bring the volumes in, I up-sample them because we found that you can re-sample the data one time like going from four milliseconds to two or two milliseconds to one and not introduce a lot of artifacts but you get better statistical information and better resolution. So I use Kingdom for some functionality but all this work is done in a completely separate program that we call Paradise.

Susan Nash: 

Thank you that was a good differentiation. So James Hill comments, it seems that your estimated production in the unconventional Casady well suggests that the majority of the reserves has been produced. Would you recommend a refrack in this well?

Deborah Sacrey:   

Well I mean what you’re not seeing on that map is all the horizontal wells have been drilled in the Meramec since that well was drilled in 1980. So we’re looking at a well that probably has very little life left in it and any life that’s left in it is probably being consumed by those wells around it which were lateral wells not shown on the map.

Susan Nash:  

Interesting. That’s good to know. Trevor Richards, this is probably our last one asks, have you seen any effects that you might link to production effects? It seems that this could be a powerful tool for characterizing stimulation areas [crosstalk] production effects in mature field areas.

Deborah Sacrey:   

Absolutely. I mean again, just because of the accuracy in the Geobodies that I’ve been able to work with, I would probably put this up against any reservoir simulation model, not only from the detail that you can see in SOM at one millisecond resolution but from the accuracy when you have a known amounts of porosity and permeability and velocity and stuff that you can get. Not that I want to become an engineer in my later life, it’s bad enough to go from a geologist to a geophysicist but I can take this tool and I can go full circle for my clients with some reliability.

Susan Nash:

Well that’s great. So we’ve come to the end of the hour and I’d like to thank you Deborah and also I’d like to thank our sponsor Geophysical Insights and really happy that they’ve sponsored this lunch and learn and I appreciate their generosity and also just like to remind everyone that you will be getting an email with the link the recording and also would like to ask you if you are interested in any other lunch and learn topics. I’d like to say thank you to Patrick Ing, and let him have a chance to say a few last words.

Patrick Ing:

Certainly Susan and Deborah really enjoyed your neurons presentation. One comment is that what you’re showing us is perhaps, like many over decades we’ve seen geologists, they’re very good at looking at things that we don’t see but this biological neurons. So what you’re showing us today is the use of these artificial neurons and combining the two you think will be better leading the arts of getting these successful maybe we develop these assets and looking for new ones.

Deborah Sacrey: 

Yeah well Patrick one last thing I’d like to say is a lot of people think that machine learning is going to get rid of interpreters, I say we’re going to need more interpreters and interpreters with experience because you still have to interpret the results of this and combine it with well information and understanding of geology to make sure that you’re on the right track and that you’re accurate. So machine learning can make life easier in terms of fault picking but we’re still going to be needed to understand everything that it’s doing.

Patrick Ing:

Thank you Deborah, thank you Susan.

Susan Nash:     

Thank you well thank you everyone and thank you again to Deborah and Patrick and we will see you on the next one.

Deborah Sacrey:  

Excellent. Thank you.

Patrick Ing:         

Bye bye.

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    Deborah SacreyOwner - Auburn Energy

    How to Use Paradise to Interpret Carbonate Reservoirs

    The key to understanding Carbonate reservoirs in Paradise start with good synthetic ties to the wavelet data. If one is not tied correctly, then it will be very east to mis-interpret the neurons as reservoir, when they are not. Secondly, the workflow should utilize Principal Component Analysis to better understand the zone of interest and the attributes to use in the SOM analysis. An important part to interpretation is understanding “Halo” and “Trailing” neurons as part of the stack around a reservoir or potential reservoir. Usually, one sees this phenomenon around deep, pressured gas reservoirs, but it can happen in shallow reservoirs as well. Two case studies are presented to emphasize the importance of looking for halo or trailing patterns around good reservoirs. One is a deep Edwards example in south central Texas, and the other a shallow oil reservoir in the Austin Chalk in the San Antonio area. Another way to help enhance carbonate reservoirs is through Spectral Decomposition. A case history is shown in the Smackover in Alabama to highlight and focus on an oolitic shoal reservoir which tunes at a specific frequency in the best wells. Not all carbonate porosity is at the top of the deposition. A case history will be discussed looking for porosity in the center portion of a reef in west Texas. And finally, one of the most difficult interpretation challenges in the carbonate spectrum is correctly mapping the interface between two carbonate layers. A simple technique is shown to help with that dilemma, by using few attributes and a low-topology count to understand regional depositional sequences. This example is from the Delaware Basin in southeastern New Mexico.

    Dr. Carrie LaudonSenior Geophysical Consultant

    Applying Unsupervised Multi-Attribute Machine Learning for 3D Stratigraphic Facies Classification in a Carbonate Field, Offshore Brazil

    We present results of a multi-attribute, machine learning study over a pre-salt carbonate field in the Santos Basin, offshore Brazil. These results test the accuracy and potential of Self-organizing maps (SOM) for stratigraphic facies delineation. The study area has an existing detailed geological facies model containing predominantly reef facies in an elongated structure.

    Carrie LaudonSenior Geophysical Consultant - Geophysical Insights

    Automatic Fault Detection and Applying Machine Learning to Detect Thin Beds

    Rapid advances in Machine Learning (ML) are transforming seismic analysis. Using these new tools, geoscientists can accomplish the following quickly and effectively:

    • Run fault detection analysis in a few hours, not weeks
    • Identify thin beds down to a single seismic sample
    • Generate seismic volumes that capture structural and stratigraphic details

    Join us for a ‘Lunch & Learn’ sessions daily at 11:00 where Dr. Carolan (“Carrie”) Laudon will review the theory and results of applying a combination of machine learning tools to obtain the above results.  A detailed agenda follows.


    Automated Fault Detection using 3D CNN Deep Learning

    • Deep learning fault detection
    • Synthetic models
    • Fault image enhancement
    • Semi-supervised learning for visualization
    • Application results
      • Normal faults
      • Fault/fracture trends in complex reservoirs

    Demo of Paradise Fault Detection Thoughtflow®

    Stratigraphic analysis using machine learning with fault detection

    • Attribute Selection using Principal Component Analysis (PCA)
    • Multi-Attribute Classification using Self-Organizing Maps (SOM)
    • Case studies – stratigraphic analysis and fault detection
      • Fault-karst and fracture examples, China
      • Niobrara – Stratigraphic analysis and thin beds, faults
    Thomas ChaparroSenior Geophysicist - Geophysical Insights

    Paradise: A Day in The Life of the Geoscientist

    Over the last several years, the industry has invested heavily in Machine Learning (ML) for better predictions and automation. Dramatic results have been realized in exploration, field development, and production optimization. However, many of these applications have been single use ‘point’ solutions. There is a growing body of evidence that seismic analysis is best served using a combination of ML tools for a specific objective, referred to as ML Orchestration. This talk demonstrates how the Paradise AI workbench applications are used in an integrated workflow to achieve superior results than traditional interpretation methods or single-purpose ML products. Using examples from combining ML-based Fault Detection and Stratigraphic Analysis, the talk will show how ML orchestration produces value for exploration and field development by the interpreter leveraging ML orchestration.

    Aldrin RondonSenior Geophysical Engineer - Dragon Oil

    Machine Learning Fault Detection: A Case Study

    An innovative Fault Pattern Detection Methodology has been carried out using a combination of Machine Learning Techniques to produce a seismic volume suitable for fault interpretation in a structurally and stratigraphic complex field. Through theory and results, the main objective was to demonstrate that a combination of ML tools can generate superior results in comparison with traditional attribute extraction and data manipulation through conventional algorithms. The ML technologies applied are a supervised, deep learning, fault classification followed by an unsupervised, multi-attribute classification combining fault probability and instantaneous attributes.

    Thomas ChaparroSenior Geophysicist - Geophysical Insights

    Thomas Chaparro is a Senior Geophysicist who specializes in training and preparing AI-based workflows. Thomas also has experience as a processing geophysicist and 2D and 3D seismic data processing. He has participated in projects in the Gulf of Mexico, offshore Africa, the North Sea, Australia, Alaska, and Brazil.

    Thomas holds a bachelor’s degree in Geology from Northern Arizona University and a Master’s in Geophysics from the University of California, San Diego. His research focus was computational geophysics and seismic anisotropy.

    Aldrin RondonSenior Geophysical Engineer - Dragon Oil

    Bachelor’s Degree in Geophysical Engineering from Central University in Venezuela with a specialization in Reservoir Characterization from Simon Bolivar University.

    Over 20 years exploration and development geophysical experience with extensive 2D and 3D seismic interpretation including acquisition and processing.

    Aldrin spent his formative years working on exploration activity in PDVSA Venezuela followed by a period working for a major international consultant company in the Gulf of Mexico (Landmark, Halliburton) as a G&G consultant. Latterly he was working at Helix in Scotland, UK on producing assets in the Central and South North Sea.  From 2007 to 2021, he has been working as a Senior Seismic Interpreter in Dubai involved in different dedicated development projects in the Caspian Sea.

    Deborah SacreyOwner - Auburn Energy

    How to Use Paradise to Interpret Clastic Reservoirs

    The key to understanding Clastic reservoirs in Paradise starts with good synthetic ties to the wavelet data. If one is not tied correctly, then it will be easy to mis-interpret the neurons as reservoir, whin they are not. Secondly, the workflow should utilize Principal Component Analysis to better understand the zone of interest and the attributes to use in the SOM analysis. An important part to interpretation is understanding “Halo” and “Trailing” neurons as part of the stack around a reservoir or potential reservoir. Deep, high-pressured reservoirs often “leak” or have vertical percolation into the seal. This changes the rock properties enough in the seal to create a “halo” effect in SOM. Likewise, the frequency changes of the seismic can cause a subtle “dim-out”, not necessarily observable in the wavelet data, but enough to create a different pattern in the Earth in terms of these rock property changes. Case histories for Halo and trailing neural information include deep, pressured, Chris R reservoir in Southern Louisiana, Frio pay in Southeast Texas and AVO properties in the Yegua of Wharton County. Additional case histories to highlight interpretation include thin-bed pays in Brazoria County, including updated information using CNN fault skeletonization. Continuing the process of interpretation is showing a case history in Wharton County on using Low Probability to help explore Wilcox reservoirs. Lastly, a look at using Paradise to help find sweet spots in unconventional reservoirs like the Eagle Ford, a case study provided by Patricia Santigrossi.

    Mike DunnSr. Vice President of Business Development

    Machine Learning in the Cloud

    Machine Learning in the Cloud will address the capabilities of the Paradise AI Workbench, featuring on-demand access enabled by the flexible hardware and storage facilities available on Amazon Web Services (AWS) and other commercial cloud services. Like the on-premise instance, Paradise On-Demand provides guided workflows to address many geologic challenges and investigations. The presentation will show how geoscientists can accomplish the following workflows quickly and effectively using guided ThoughtFlows® in Paradise:

    • Identify and calibrate detailed stratigraphy using seismic and well logs
    • Classify seismic facies
    • Detect faults automatically
    • Distinguish thin beds below conventional tuning
    • Interpret Direct Hydrocarbon Indicators
    • Estimate reserves/resources

    Attend the talk to see how ML applications are combined through a process called "Machine Learning Orchestration," proven to extract more from seismic and well data than traditional means.

    Sarah Stanley
    Senior Geoscientist

    Stratton Field Case Study – New Solutions to Old Problems

    The Oligocene Frio gas-producing Stratton Field in south Texas is a well-known field. Like many onshore fields, the productive sand channels are difficult to identify using conventional seismic data. However, the productive channels can be easily defined by employing several Paradise modules, including unsupervised machine learning, Principal Component Analysis, Self-Organizing Maps, 3D visualization, and the new Well Log Cross Section and Well Log Crossplot tools. The Well Log Cross Section tool generates extracted seismic data, including SOMs, along the Cross Section boreholes and logs. This extraction process enables the interpreter to accurately identify the SOM neurons associated with pay versus neurons associated with non-pay intervals. The reservoir neurons can be visualized throughout the field in the Paradise 3D Viewer, with Geobodies generated from the neurons. With this ThoughtFlow®, pay intervals previously difficult to see in conventional seismic can finally be visualized and tied back to the well data.

    Laura Cuttill
    Practice Lead, Advertas

    Young Professionals – Managing Your Personal Brand to Level-up Your Career

    No matter where you are in your career, your online “personal brand” has a huge impact on providing opportunity for prospective jobs and garnering the respect and visibility needed for advancement. While geoscientists tackle ambitious projects, publish in technical papers, and work hard to advance their careers, often, the value of these isn’t realized beyond their immediate professional circle. Learn how to…

    • - Communicate who you are to high-level executives in exploration and development
    • - Avoid common social media pitfalls
    • - Optimize your online presence to best garner attention from recruiters
    • - Stay relevant
    • - Create content of interest
    • - Establish yourself as a thought leader in your given area of specialization
    Laura Cuttill
    Practice Lead, Advertas

    As a 20-year marketing veteran marketing in oil and gas and serial entrepreneur, Laura has deep experience in bringing technology products to market and growing sales pipeline. Armed with a marketing degree from Texas A&M, she began her career doing technical writing for Schlumberger and ExxonMobil in 2001. She started Advertas as a co-founder in 2004 and began to leverage her upstream experience in marketing. In 2006, she co-founded the cyber-security software company, 2FA Technology. After growing 2FA from a startup to 75% market share in target industries, and the subsequent sale of the company, she returned to Advertas to continue working toward the success of her clients, such as Geophysical Insights. Today, she guides strategy for large-scale marketing programs, manages project execution, cultivates relationships with industry media, and advocates for data-driven, account-based marketing practices.

    Fabian Rada
    Sr. Geophysicist, Petroleum Oil & Gas Services

    Statistical Calibration of SOM results with Well Log Data (Case Study)

    The first stage of the proposed statistical method has proven to be very useful in testing whether or not there is a relationship between two qualitative variables (nominal or ordinal) or categorical quantitative variables, in the fields of health and social sciences. Its application in the oil industry allows geoscientists not only to test dependence between discrete variables, but to measure their degree of correlation (weak, moderate or strong). This article shows its application to reveal the relationship between a SOM classification volume of a set of nine seismic attributes (whose vertical sampling interval is three meters) and different well data (sedimentary facies, Net Reservoir, and effective porosity grouped by ranges). The data were prepared to construct the contingency tables, where the dependent (response) variable and independent (explanatory) variable were defined, the observed frequencies were obtained, and the frequencies that would be expected if the variables were independent were calculated and then the difference between the two magnitudes was studied using the contrast statistic called Chi-Square. The second stage implies the calibration of the SOM volume extracted along the wellbore path through statistical analysis of the petrophysical properties VCL and PHIE, and SW for each neuron, which allowed to identify the neurons with the best petrophysical values in a carbonate reservoir.

    Heather Bedle
    Assistant Professor, University of Oklahoma

    Heather Bedle received a B.S. (1999) in physics from Wake Forest University, and then worked as a systems engineer in the defense industry. She later received a M.S. (2005) and a Ph. D. (2008) degree from Northwestern University. After graduate school, she joined Chevron and worked as both a development geologist and geophysicist in the Gulf of Mexico before joining Chevron’s Energy Technology Company Unit in Houston, TX. In this position, she worked with the Rock Physics from Seismic team analyzing global assets in Chevron’s portfolio. Dr. Bedle is currently an assistant professor of applied geophysics at the University of Oklahoma’s School of Geosciences. She joined OU in 2018, after instructing at the University of Houston for two years. Dr. Bedle and her student research team at OU primarily work with seismic reflection data, using advanced techniques such as machine learning, attribute analysis, and rock physics to reveal additional structural, stratigraphic and tectonic insights of the subsurface.

    Jie Qi
    Research Geophysicist

    An Integrated Fault Detection Workflow

    Seismic fault detection is one of the top critical procedures in seismic interpretation. Identifying faults are significant for characterizing and finding the potential oil and gas reservoirs. Seismic amplitude data exhibiting good resolution and a high signal-to-noise ratio are key to identifying structural discontinuities using seismic attributes or machine learning techniques, which in turn serve as input for automatic fault extraction. Deep learning Convolutional Neural Networks (CNN) performs well on fault detection without any human-computer interactive work. This study shows an integrated CNN-based fault detection workflow to construct fault images that are sufficiently smooth for subsequent fault automatic extraction. The objectives were to suppress noise or stratigraphic anomalies subparallel to reflector dip, and sharpen fault and other discontinuities that cut reflectors, preconditioning the fault images for subsequent automatic extraction. A 2D continuous wavelet transform-based acquisition footprint suppression method was applied time slice by time slice to suppress wavenumber components to avoid interpreting the acquisition footprint as artifacts by the CNN fault detection method. To further suppress cross-cutting noise as well as sharpen fault edges, a principal component edge-preserving structure-oriented filter is also applied. The conditioned amplitude volume is then fed to a pre-trained CNN model to compute fault probability. Finally, a Laplacian of Gaussian filter is applied to the original CNN fault probability to enhance fault images. The resulting fault probability volume is favorable with respect to traditional human-interpreter generated on vertical slices through the seismic amplitude volume.

    Dr. Jie Qi
    Research Geophysicist

    An integrated machine learning-based fault classification workflow

    We introduce an integrated machine learning-based fault classification workflow that creates fault component classification volumes that greatly reduces the burden on the human interpreter. We first compute a 3D fault probability volume from pre-conditioned seismic amplitude data using a 3D convolutional neural network (CNN). However, the resulting “fault probability” volume delineates other non-fault edges such as angular unconformities, the base of mass transport complexes, and noise such as acquisition footprint. We find that image processing-based fault discontinuity enhancement and skeletonization methods can enhance the fault discontinuities and suppress many of the non-fault discontinuities. Although each fault is characterized by its dip and azimuth, these two properties are discontinuous at azimuths of φ=±180° and for near vertical faults for azimuths φ and φ+180° requiring them to be parameterized as four continuous geodetic fault components. These four fault components as well as the fault probability can then be fed into a self-organizing map (SOM) to generate fault component classification. We find that the final classification result can segment fault sets trending in interpreter-defined orientations and minimize the impact of stratigraphy and noise by selecting different neurons from the SOM 2D neuron color map.

    Ivan Marroquin
    Senior Research Geophysicist

    Connecting Multi-attribute Classification to Reservoir Properties

    Interpreters rely on seismic pattern changes to identify and map geologic features of importance. The ability to recognize such features depends on the seismic resolution and characteristics of seismic waveforms. With the advancement of machine learning algorithms, new methods for interpreting seismic data are being developed. Among these algorithms, self-organizing maps (SOM) provides a different approach to extract geological information from a set of seismic attributes.

    SOM approximates the input patterns by a finite set of processing neurons arranged in a regular 2D grid of map nodes. Such that, it classifies multi-attribute seismic samples into natural clusters following an unsupervised approach. Since machine learning is unbiased, so the classifications can contain both geological information and coherent noise. Thus, seismic interpretation evolves into broader geologic perspectives. Additionally, SOM partitions multi-attribute samples without a priori information to guide the process (e.g., well data).

    The SOM output is a new seismic attribute volume, in which geologic information is captured from the classification into winning neurons. Implicit and useful geological information are uncovered through an interactive visual inspection of winning neuron classifications. By doing so, interpreters build a classification model that aids them to gain insight into complex relationships between attribute patterns and geological features.

    Despite all these benefits, there are interpretation challenges regarding whether there is an association between winning neurons and geological features. To address these issues, a bivariate statistical approach is proposed. To evaluate this analysis, three cases scenarios are presented. In each case, the association between winning neurons and net reservoir (determined from petrophysical or well log properties) at well locations is analyzed. The results show that the statistical analysis not only aid in the identification of classification patterns; but more importantly, reservoir/not reservoir classification by classical petrophysical analysis strongly correlates with selected SOM winning neurons. Confidence in interpreted classification features is gained at the borehole and interpretation is readily extended as geobodies away from the well.

    Heather Bedle
    Assistant Professor, University of Oklahoma

    Gas Hydrates, Reefs, Channel Architecture, and Fizz Gas: SOM Applications in a Variety of Geologic Settings

    Students at the University of Oklahoma have been exploring the uses of SOM techniques for the last year. This presentation will review learnings and results from a few of these research projects. Two projects have investigated the ability of SOMs to aid in identification of pore space materials – both trying to qualitatively identify gas hydrates and under-saturated gas reservoirs. A third study investigated individual attributes and SOMs in recognizing various carbonate facies in a pinnacle reef in the Michigan Basin. The fourth study took a deep dive of various machine learning algorithms, of which SOMs will be discussed, to understand how much machine learning can aid in the identification of deepwater channel architectures.

    Fabian Rada
    Sr. Geophysicist, Petroleum Oil & Gas Servicest

    Fabian Rada joined Petroleum Oil and Gas Services, Inc (POGS) in January 2015 as Business Development Manager and Consultant to PEMEX. In Mexico, he has participated in several integrated oil and gas reservoir studies. He has consulted with PEMEX Activos and the G&G Technology group to apply the Paradise AI workbench and other tools. Since January 2015, he has been working with Geophysical Insights staff to provide and implement the multi-attribute analysis software Paradise in Petróleos Mexicanos (PEMEX), running a successful pilot test in Litoral Tabasco Tsimin Xux Asset. Mr. Rada began his career in the Venezuelan National Foundation for Seismological Research, where he participated in several geophysical projects, including seismic and gravity data for micro zonation surveys. He then joined China National Petroleum Corporation (CNPC) as QC Geophysicist until he became the Chief Geophysicist in the QA/QC Department. Then, he transitioned to a subsidiary of Petróleos de Venezuela (PDVSA), as a member of the QA/QC and Chief of Potential Field Methods section. Mr. Rada has also participated in processing land seismic data and marine seismic/gravity acquisition surveys. Mr. Rada earned a B.S. in Geophysics from the Central University of Venezuela.

    Hal GreenDirector, Marketing & Business Development - Geophysical Insights

    Introduction to Automatic Fault Detection and Applying Machine Learning to Detect Thin Beds

    Rapid advances in Machine Learning (ML) are transforming seismic analysis. Using these new tools, geoscientists can accomplish the following quickly and effectively: a combination of machine learning (ML) and deep learning applications, geoscientists apply Paradise to extract greater insights from seismic and well data for these and other objectives:

    • Run fault detection analysis in a few hours, not weeks
    • Identify thin beds down to a single seismic sample
    • Overlay fault images on stratigraphic analysis

    The brief introduction will orient you with the technology and examples of how machine learning is being applied to automate interpretation while generating new insights in the data.

    Sarah Stanley
    Senior Geoscientist and Lead Trainer

    Sarah Stanley joined Geophysical Insights in October, 2017 as a geoscience consultant, and became a full-time employee July 2018. Prior to Geophysical Insights, Sarah was employed by IHS Markit in various leadership positions from 2011 to her retirement in August 2017, including Director US Operations Training and Certification, the Operational Governance Team, and, prior to February 2013, Director of IHS Kingdom Training. Sarah joined SMT in May, 2002, and was the Director of Training for SMT until IHS Markit’s acquisition in 2011.

    Prior to joining SMT Sarah was employed by GeoQuest, a subdivision of Schlumberger, from 1998 to 2002. Sarah was also Director of the Geoscience Technology Training Center, North Harris College from 1995 to 1998, and served as a voluntary advisor on geoscience training centers to various geological societies. Sarah has over 37 years of industry experience and has worked as a petroleum geoscientist in various domestic and international plays since August of 1981. Her interpretation experience includes tight gas sands, coalbed methane, international exploration, and unconventional resources.

    Sarah holds a Bachelor’s of Science degree with majors in Biology and General Science and minor in Earth Science, a Master’s of Arts in Education and Master’s of Science in Geology from Ball State University, Muncie, Indiana. Sarah is both a Certified Petroleum Geologist, and a Registered Geologist with the State of Texas. Sarah holds teaching credentials in both Indiana and Texas.

    Sarah is a member of the Houston Geological Society and the American Association of Petroleum Geologists, where she currently serves in the AAPG House of Delegates. Sarah is a recipient of the AAPG Special Award, the AAPG House of Delegates Long Service Award, and the HGS President’s award for her work in advancing training for petroleum geoscientists. She has served on the AAPG Continuing Education Committee and was Chairman of the AAPG Technical Training Center Committee. Sarah has also served as Secretary of the HGS, and Served two years as Editor for the AAPG Division of Professional Affairs Correlator.

    Dr. Tom Smith
    President & CEO

    Dr. Tom Smith received a BS and MS degree in Geology from Iowa State University. His graduate research focused on a shallow refraction investigation of the Manson astrobleme. In 1971, he joined Chevron Geophysical as a processing geophysicist but resigned in 1980 to complete his doctoral studies in 3D modeling and migration at the Seismic Acoustics Lab at the University of Houston. Upon graduation with the Ph.D. in Geophysics in 1981, he started a geophysical consulting practice and taught seminars in seismic interpretation, seismic acquisition and seismic processing. Dr. Smith founded Seismic Micro-Technology in 1984 to develop PC software to support training workshops which subsequently led to development of the KINGDOM Software Suite for integrated geoscience interpretation with world-wide success.

    The Society of Exploration Geologists (SEG) recognized Dr. Smith’s work with the SEG Enterprise Award in 2000, and in 2010, the Geophysical Society of Houston (GSH) awarded him an Honorary Membership. Iowa State University (ISU) has recognized Dr. Smith throughout his career with the Distinguished Alumnus Lecturer Award in 1996, the Citation of Merit for National and International Recognition in 2002, and the highest alumni honor in 2015, the Distinguished Alumni Award. The University of Houston College of Natural Sciences and Mathematics recognized Dr. Smith with the 2017 Distinguished Alumni Award.

    In 2009, Dr. Smith founded Geophysical Insights, where he leads a team of geophysicists, geologists and computer scientists in developing advanced technologies for fundamental geophysical problems. The company launched the Paradise® multi-attribute analysis software in 2013, which uses Machine Learning and pattern recognition to extract greater information from seismic data.

    Dr. Smith has been a member of the SEG since 1967 and is a professional member of SEG, GSH, HGS, EAGE, SIPES, AAPG, Sigma XI, SSA and AGU. Dr. Smith served as Chairman of the SEG Foundation from 2010 to 2013. On January 25, 2016, he was recognized by the Houston Geological Society (HGS) as a geophysicist who has made significant contributions to the field of geology. He currently serves on the SEG President-Elect’s Strategy and Planning Committee and the ISU Foundation Campaign Committee for Forever True, For Iowa State.

    Carrie LaudonSenior Geophysical Consultant - Geophysical Insights

    Applying Machine Learning Technologies in the Niobrara Formation, DJ Basin, to Quickly Produce an Integrated Structural and Stratigraphic Seismic Classification Volume Calibrated to Wells

    This study will demonstrate an automated machine learning approach for fault detection in a 3D seismic volume. The result combines Deep Learning Convolution Neural Networks (CNN) with a conventional data pre-processing step and an image processing-based post processing approach to produce high quality fault attribute volumes of fault probability, fault dip magnitude and fault dip azimuth. These volumes are then combined with instantaneous attributes in an unsupervised machine learning classification, allowing the isolation of both structural and stratigraphic features into a single 3D volume. The workflow is illustrated on a 3D seismic volume from the Denver Julesburg Basin and a statistical analysis is used to calibrate results to well data.

    Ivan Marroquin
    Senior Research Geophysicist

    Iván Dimitri Marroquín is a 20-year veteran of data science research, consistently publishing in peer-reviewed journals and speaking at international conference meetings. Dr. Marroquín received a Ph.D. in geophysics from McGill University, where he conducted and participated in 3D seismic research projects. These projects focused on the development of interpretation techniques based on seismic attributes and seismic trace shape information to identify significant geological features or reservoir physical properties. Examples of his research work are attribute-based modeling to predict coalbed thickness and permeability zones, combining spectral analysis with coherency imagery technique to enhance interpretation of subtle geologic features, and implementing a visual-based data mining technique on clustering to match seismic trace shape variability to changes in reservoir properties.

    Dr. Marroquín has also conducted some ground-breaking research on seismic facies classification and volume visualization. This lead to his development of a visual-based framework that determines the optimal number of seismic facies to best reveal meaningful geologic trends in the seismic data. He proposed seismic facies classification as an alternative to data integration analysis to capture geologic information in the form of seismic facies groups. He has investigated the usefulness of mobile devices to locate, isolate, and understand the spatial relationships of important geologic features in a context-rich 3D environment. In this work, he demonstrated mobile devices are capable of performing seismic volume visualization, facilitating the interpretation of imaged geologic features.  He has definitively shown that mobile devices eventually will allow the visual examination of seismic data anywhere and at any time.

    In 2016, Dr. Marroquín joined Geophysical Insights as a senior researcher, where his efforts have been focused on developing machine learning solutions for the oil and gas industry. For his first project, he developed a novel procedure for lithofacies classification that combines a neural network with automated machine methods. In parallel, he implemented a machine learning pipeline to derive cluster centers from a trained neural network. The next step in the project is to correlate lithofacies classification to the outcome of seismic facies analysis.  Other research interests include the application of diverse machine learning technologies for analyzing and discerning trends and patterns in data related to oil and gas industry.

    Dr. Jie Qi
    Research Geophysicist

    Dr. Jie Qi is a Research Geophysicist at Geophysical Insights, where he works closely with product development and geoscience consultants. His research interests include machine learning-based fault detection, seismic interpretation, pattern recognition, image processing, seismic attribute development and interpretation, and seismic facies analysis. Dr. Qi received a BS (2011) in Geoscience from the China University of Petroleum in Beijing, and an MS (2013) in Geophysics from the University of Houston. He earned a Ph.D. (2017) in Geophysics from the University of Oklahoma, Norman. His industry experience includes work as a Research Assistant (2011-2013) at the University of Houston and the University of Oklahoma (2013-2017). Dr. Qi was with Petroleum Geo-Services (PGS), Inc. in 2014 as a summer intern, where he worked on a semi-supervised seismic facies analysis. In 2017, he served as a postdoctoral Research Associate in the Attributed Assisted-Seismic Processing and Interpretation (AASPI) consortium at the University of Oklahoma from 2017 to 2020.

    Rocky R. Roden
    Senior Consulting Geophysicist

    The Relationship of Self-Organization, Geology, and Machine Learning

    Self-organization is the nonlinear formation of spatial and temporal structures, patterns or functions in complex systems (Aschwanden et al., 2018). Simple examples of self-organization include flocks of birds, schools of fish, crystal development, formation of snowflakes, and fractals. What these examples have in common is the appearance of structure or patterns without centralized control. Self-organizing systems are typically governed by power laws, such as the Gutenberg-Richter law of earthquake frequency and magnitude. In addition, the time frames of such systems display a characteristic self-similar (fractal) response, where earthquakes or avalanches for example, occur over all possible time scales (Baas, 2002).

    The existence of nonlinear dynamic systems and ordered structures in the earth are well known and have been studied for centuries and can appear as sedimentary features, layered and folded structures, stratigraphic formations, diapirs, eolian dune systems, channelized fluvial and deltaic systems, and many more (Budd, et al., 2014; Dietrich and Jacob, 2018). Each of these geologic processes and features exhibit patterns through the action of undirected local dynamics and is generally termed “self-organization” (Paola, 2014).

    Artificial intelligence and specifically neural networks exhibit and reveal self-organization characteristics. The reason for the interest in applying neural networks stems from the fact that they are universal approximators for various kinds of nonlinear dynamical systems of arbitrary complexity (Pessa, 2008). A special class of artificial neural networks is aptly named self-organizing map (SOM) (Kohonen, 1982). It has been found that SOM can identify significant organizational structure in the form of clusters from seismic attributes that relate to geologic features (Strecker and Uden, 2002; Coleou et al., 2003; de Matos, 2006; Roy et al., 2013; Roden et al., 2015; Zhao et al., 2016; Roden et al., 2017; Zhao et al., 2017; Roden and Chen, 2017; Sacrey and Roden, 2018; Leal et al, 2019; Hussein et al., 2020; Hardage et al., 2020; Manauchehri et al., 2020). As a consequence, SOM is an excellent machine learning neural network approach utilizing seismic attributes to help identify self-organization features and define natural geologic patterns not easily seen or seen at all in the data.

    Rocky R. Roden
    Senior Consulting Geophysicist

    Rocky R. Roden started his own consulting company, Rocky Ridge Resources Inc. in 2003 and works with several oil companies on technical and prospect evaluation issues. He is also a principal in the Rose and Associates DHI Risk Analysis Consortium and was Chief Consulting Geophysicist with Seismic Micro-technology. Rocky is a proven oil finder with 37 years in the industry, gaining extensive knowledge of modern geoscience technical approaches.

    Rocky holds a BS in Oceanographic Technology-Geology from Lamar University and a MS in Geological and Geophysical Oceanography from Texas A&M University. As Chief Geophysicist and Director of Applied Technology for Repsol-YPF, his role comprised of advising corporate officers, geoscientists, and managers on interpretation, strategy and technical analysis for exploration and development in offices in the U.S., Argentina, Spain, Egypt, Bolivia, Ecuador, Peru, Brazil, Venezuela, Malaysia, and Indonesia. He has been involved in the technical and economic evaluation of Gulf of Mexico lease sales, farmouts worldwide, and bid rounds in South America, Europe, and the Far East. Previous work experience includes exploration and development at Maxus Energy, Pogo Producing, Decca Survey, and Texaco. Rocky is a member of SEG, AAPG, HGS, GSH, EAGE, and SIPES; he is also a past Chairman of The Leading Edge Editorial Board.

    Bob A. Hardage

    Bob A. Hardage received a PhD in physics from Oklahoma State University. His thesis work focused on high-velocity micro-meteoroid impact on space vehicles, which required trips to Goddard Space Flight Center to do finite-difference modeling on dedicated computers. Upon completing his university studies, he worked at Phillips Petroleum Company for 23 years and was Exploration Manager for Asia and Latin America when he left Phillips. He moved to WesternAtlas and worked 3 years as Vice President of Geophysical Development and Marketing. He then established a multicomponent seismic research laboratory at the Bureau of Economic Geology and served The University of Texas at Austin as a Senior Research Scientist for 28 years. He has published books on VSP, cross-well profiling, seismic stratigraphy, and multicomponent seismic technology. He was the first person to serve 6 years on the Board of Directors of the Society of Exploration Geophysicists (SEG). His Board service was as SEG Editor (2 years), followed by 1-year terms as First VP, President Elect, President, and Past President. SEG has awarded him a Special Commendation, Life Membership, and Honorary Membership. He wrote the AAPG Explorer column on geophysics for 6 years. AAPG honored him with a Distinguished Service award for promoting geophysics among the geological community.

    Bob A. Hardage

    Investigating the Internal Fabric of VSP data with Attribute Analysis and Unsupervised Machine Learning

    Examination of vertical seismic profile (VSP) data with unsupervised machine learning technology is a rigorous way to compare the fabric of down-going, illuminating, P and S wavefields with the fabric of up-going reflections and interbed multiples created by these wavefields. This concept is introduced in this paper by applying unsupervised learning to VSP data to better understand the physics of P and S reflection seismology. The zero-offset VSP data used in this investigation were acquired in a hard-rock, fast-velocity, environment that caused the shallowest 2 or 3 geophones to be inside the near-field radiation zone of a vertical-vibrator baseplate. This study shows how to use instantaneous attributes to backtrack down-going direct-P and direct-S illuminating wavelets to the vibrator baseplate inside the near-field zone. This backtracking confirms that the points-of-origin of direct-P and direct-S are identical. The investigation then applies principal component (PCA) analysis to VSP data and shows that direct-S and direct-P wavefields that are created simultaneously at a vertical-vibrator baseplate have the same dominant principal components. A self-organizing map (SOM) approach is then taken to illustrate how unsupervised machine learning describes the fabric of down-going and up-going events embedded in vertical-geophone VSP data. These SOM results show that a small number of specific neurons build the down-going direct-P illuminating wavefield, and another small group of neurons build up-going P primary reflections and early-arriving down-going P multiples. The internal attribute fabric of these key down-going and up-going neurons are then compared to expose their similarities and differences. This initial study indicates that unsupervised machine learning, when applied to VSP data, is a powerful tool for understanding the physics of seismic reflectivity at a prospect. This research strategy of analyzing VSP data with unsupervised machine learning will now expand to horizontal-geophone VSP data.

    Tom Smith
    President and CEO, Geophysical Insights

    Machine Learning for Incomplete Geoscientists

    This presentation covers big-picture machine learning buzz words with humor and unassailable frankness. The goal of the material is for every geoscientist to gain confidence in these important concepts and how they add to our well-established practices, particularly seismic interpretation. Presentation topics include a machine learning historical perspective, what makes it different, a fish factory, Shazam, comparison of supervised and unsupervised machine learning methods with examples, tuning thickness, deep learning, hard/soft attribute spaces, multi-attribute samples, and several interpretation examples. After the presentation, you may not know how to run machine learning algorithms, but you should be able to appreciate their value and avoid some of their limitations.

    Deborah Sacrey
    Owner, Auburn Energy

    Deborah is a geologist/geophysicist with 44 years of oil and gas exploration experience in Texas, Louisiana Gulf Coast and Mid-Continent areas of the US. She received her degree in Geology from the University of Oklahoma in 1976 and immediately started working for Gulf Oil in their Oklahoma City offices.

    She started her own company, Auburn Energy, in 1990 and built her first geophysical workstation using Kingdom software in 1996. She helped SMT/IHS for 18 years in developing and testing the Kingdom Software. She specializes in 2D and 3D interpretation for clients in the US and internationally. For the past nine 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 public, guided by Dr. Tom Smith, founder of SMT. She has become an expert in the use of Paradise software and has seven discoveries for clients using multi-attribute neural analysis.

    Deborah has been 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 also Past President of the Gulf Coast Association of Geological Societies and just ended a term as one of the GCAGS representatives on the AAPG Advisory Council. Deborah is also a DPA Certified Petroleum Geologist #4014 and DPA Certified Petroleum Geophysicist #2. She belongs to AAPG, SIPES, Houston Geological Society, South Texas Geological Society and the Oklahoma City Geological Society (OCGS).

    Mike Dunn
    Senior Vice President Business Development

    Michael A. Dunn is an exploration executive with extensive global experience including the Gulf of Mexico, Central America, Australia, China and North Africa. Mr. Dunn has a proven a track record of successfully executing exploration strategies built on a foundation of new and innovative technologies. Currently, Michael serves as Senior Vice President of Business Development for Geophysical Insights.

    He joined Shell in 1979 as an exploration geophysicist and party chief and held increasing levels or responsibility including Manager of Interpretation Research. In 1997, he participated in the launch of Geokinetics, which completed an IPO on the AMEX in 2007. His extensive experience with oil companies (Shell and Woodside) and the service sector (Geokinetics and Halliburton) has provided him with a unique perspective on technology and applications in oil and gas. Michael received a B.S. in Geology from Rutgers University and an M.S. in Geophysics from the University of Chicago.

    Hal GreenDirector, Marketing & Business Development - Geophysical Insights

    Hal H. Green is a marketing executive and entrepreneur in the energy industry with more than 25 years of experience in starting and managing technology companies. He holds a B.S. in Electrical Engineering from Texas A&M University and an MBA from the University of Houston. He has invested his career at the intersection of marketing and technology, with a focus on business strategy, marketing, and effective selling practices. Mr. Green has a diverse portfolio of experience in marketing technology to the hydrocarbon supply chain – from upstream exploration through downstream refining & petrochemical. Throughout his career, Mr. Green has been a proven thought-leader and entrepreneur, while supporting several tech start-ups.

    He started his career as a process engineer in the semiconductor manufacturing industry in Dallas, Texas and later launched an engineering consulting and systems integration business. Following the sale of that business in the late 80’s, he joined Setpoint in Houston, Texas where he eventually led that company’s Manufacturing Systems business. Aspen Technology acquired Setpoint in January 1996 and Mr. Green continued as Director of Business Development for the Information Management and Polymer Business Units.

    In 2004, Mr. Green founded Advertas, a full-service marketing and public relations firm serving clients in energy and technology. In 2010, Geophysical Insights retained Advertas as their marketing firm. Dr. Tom Smith, President/CEO of Geophysical Insights, soon appointed Mr. Green as Director of Marketing and Business Development for Geophysical Insights, in which capacity he still serves today.

    Hana Kabazi
    Product Manager

    Hana Kabazi joined Geophysical Insights in October of 201, and is now one of our Product Managers for Paradise. Mrs. Kabazi has over 7 years of oil and gas experience, including 5 years and Halliburton – Landmark. During her time at Landmark she held positions as a consultant to many E&P companies, technical advisor to the QA organization, and as product manager of Subsurface Mapping in DecsionSpace. Mrs. Kabazi has a B.S. in Geology from the University of Texas Austin, and an M.S. in Geology from the University of Houston.

    Dr. Carrie LaudonSenior Geophysical Consultant - Geophysical Insights

    Carolan (Carrie) Laudon holds a PhD in geophysics from the University of Minnesota and a BS in geology from the University of Wisconsin Eau Claire. She has been Senior Geophysical Consultant with Geophysical Insights since 2017 working with Paradise®, their machine learning platform. Prior roles include Vice President of Consulting Services and Microseismic Technology for Global Geophysical Services and 17 years with Schlumberger in technical, management and sales, starting in Alaska and including Aberdeen, Scotland, Houston, TX, Denver, CO and Reading, England. She spent five years early in her career with ARCO Alaska as a seismic interpreter for the Central North Slope exploration team.