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Drilling & Completions Passion for Change

Passion for Change: Colorado School of Mines

with faculty from the Colorado School of Mines Dr. Bill Eustes, Associate Professor, Petroleum Engineering and Jim Crompton, Professor of Practice, Petroleum Engineering

Special thanks to Ronnie Arispe, Data and Analytics Specialist at Concho, and Anthony Bordonaro, Production Technologist at Chevron, from the SPE Permian Basin Section for helping to conduct this interview. The Permian Basin Section has been recognized by SPE with the 2019 Section Excellence Award in recognition of the section’s hard work and strong programs in industry engagement, operation and planning, community involvement, professional development and innovation.

Tell us about your background.

JC: I’m something called a Professor of Practice in the Petroleum Engineering Department at the Colorado School of Mines, somebody who got his lumps from a number of decades in the industry rather than a PhD. 

I am relatively new to the faculty, although I go back way to 1974 at the School of Mines when I got my degree in geophysical engineering. After getting my Master’s, I joined Chevron Oil Company where I spent the next 37 years. One company, one paycheck, but a number of different careers from traditional seismic processing, seismic interpretation, and then I finished the last third of my career in the area of digital oilfields, or integrated oilfields, as it was called at Chevron at the time. 

I retired in 2013 and moved back to Colorado. Four years ago, I was asked to create a capstone course for a Data Analytics Minor within the Petroleum Engineering program.

BE: I’m Bill Eustes. I have spent 42 years in this business. I graduated from Louisiana Tech back in 1978 with a Bachelor of Science in mechanical engineering. I went to work at ARCO Oil and Gas working as a drilling engineer out in Hobbs, New Mexico. Then I did a stint in Midland, so I’ve had the experience of living in the Permian Basin. Then I worked as a drilling engineer out of the Midcontinent District in Tulsa as well as in the East Texas and North Louisiana area, and then finally went to Enid, Oklahoma where I was a production engineer until 1987. 

At that time, I recall ARCO getting a spreadsheet program called Lotus 1-2-3.  We loaded the specs on all of our wells on it. When the market crashed in ‘85 and ‘86, we went through there and populated it and said, “What is our break-even point for the price of oil for each well?” I remember this was just an awesome event to be able to go through 2,500 wells and then sort it and see which wells were making money. That was an amazing epiphany to be able to look at something like that.

Another thing that stuck with me—there was this really deep well in 1982 that I was involved with in Oklahoma while working for ARCO. I remember a company called ExLog that did mud logging; and, they would print out all of the specifications of the drilling operations on one of those old tractor feed type of printers.  I remember looking at stacks of paper and wondering what I was going to do with it. I could see some value, but it wasn’t any sort of format that we could use. 

That’s always been in the back of my mind: how do I use this information to be able to do a better job?

And then I got laid off. 

In hindsight, that was the best thing, because I got to choose my own pathway forward. I decided I wanted to get more education. I went to the University of Colorado Boulder and have a Master of Science degree in Mechanical Engineering. I thought I’d change the industry I worked in, but when you start looking at your bloodstream when you’ve been in this business, it’s no longer blood— it’s oil.

It just so happens there was a school right down the road from CU-Boulder that had a Petroleum Engineering program. That’s how I wound up at the Colorado School of Mines as a graduate student. I spent six years as a graduate student in various areas of research including the Yucca Mountain project, the Hanford nuclear waste site, places like that.

I had my advisor retire right as I finished, so I put my name in the hat, and lo and behold, here I am 24 years later. It’s been a wild ride!

What do you do at the Colorado School of Mines and what makes your work unique?

JC: I think one of the things that Bill and I share is the passion to apply data to do something useful—drill a better well, have better production, artificial lift optimization, whatever it is. Through our individual four decades of experience, we’ve seen this data become more plentiful. We’ve seen this data become a little bit easier to use. We’ve seen better tools crop up. So, it’s getting closer and closer to being able to do decision-making analysis. 

It isn’t the company with the most data that wins. It’s a company that makes the best decisions from the data they have that wins. 

I think both of us share this idea of trying to instill into the next generation workforce their understanding of the data and then what you can do with it. It’s not an overemphasis on sensors or IOT or cloud computing or whatever. It’s the idea of application. 

We talk a lot about understanding data. We talk a lot about data visualization. Forty years ago, when I was on campus, a petroleum engineer wouldn’t go beyond Excel spreadsheets. Now, we’ve got R and Python programming and it’s a new world of the capabilities, a new generation of digital engineers.

BE: We now have the tools, but you know the famous phrase, “All models are wrong, but some are useful.” [AE1] We’re trying to build more useful models.

The machines are there to assist you, to augment you in being able to make decisions. They’re not there to make the decisions for you. 

We’re working on a certificate program for those that are at the postgraduate level, whether it be in a Masters or PhD program, or just somebody out in Industry interested in wanting to get a better understanding of how to be a digital engineer- actually working on projects in drilling, production, reservoir, and unconventional resources. At the end of the 12 credit-hour sequence, you would have a Graduate Level certificate in Petroleum Data Analytics from the Colorado School of Mines. 

We’re also looking at automation, developing really good high-quality data and models that can be able to tell the machine where things should be going. 

That’s one of the things I personally am looking at, deriving insights into making our operations better. But also looking at a longer-term goal of trying to see what areas we can automate and make things safer and more reliable and more consistent.

I’m part of the Drilling Systems Automation Technology Section of the SPE. One of our drivers is developing methodologies to be able to automate our drilling rigs for consistency as well as safety. A well-trained crew can beat a machine right now, but they can only last so long before they wear out, and of course, finding a well-trained crew might be a challenge these days with the loss of experience that we’re unfortunately seeing. So perhaps this is a way to help us drill wells better and safer.

We need to start with what kind of problem you’re solving and then need to understand what kind of data you’re using and tell a good story with the data, but at the same time, talk about what you could do with the data. It isn’t just data crunching. The model has to go beyond just telling you what’s happened. The challenge for petroleum is to figure out what’s going to happen in the future, not just what was my production today. Can you give me an accurate forecast for my production in the next three to six months so I can go to the shareholder meeting and tell them how much money we’re going to make?

JC: To help older graduates, we’ve developed a graduate certificate program for more mature engineering people practicing in the industry to take in the evenings and on the weekends. We think we can add value for a modest commitment to engineers at any level, even if you just take it to learn the language, you get some hands-on experience with the tools. We’re not turning petroleum engineers into programmers, but students learn basic scripting programming languages like Python and R.

BE: Something that’s kind of unique is that we have a drill and we actually collect our own data. It’s actually a mining coring rig and we have sensors all over it so that we can actually collect the core as we are drilling and record the data. The idea is that you collect and analyze your own data. I want to see how students handle this large volume of data: 20,000 Hertz in 10 minutes from two tri-axial sensors, being able to deal with that, and see the pitfalls and the promises of being able to handle that information, and what it tells you.

JC: There comes a moment in every young digital petroleum engineer’s career where they break Excel, and we want to give them that experience early so they can realize what’s on the other side of it, the new tools and new technologies that will help them build those models with that volume of data, variety of data, and velocity of data.

Do you see any gaps in the tools being used today? What do you think the tools of the future could look like?

JC: We’re building billion-cell reservoir simulations instead of a few thousand cells. Streaming analytics as well as spatial analytics are two areas that I think we’re moving into and it has to do with the variety of data and velocity of data. Maybe we don’t know exactly what to do with 20,000 Hertz, but we could if we could just downsize that to a thousand Hertz. That’s a lot of data. Can we then have a feedback loop where the model is learning from data? 

As we’re drilling a well, if that model gets updated, it could become a better predictor, and then we can find that potential stuck pipe problem, or we could find the fact that we’re going to break off a tooth on the drill bit and avoid an unnecessary trip to set another casing string. Right now, we’re trying to do the best we can, which means we’re probably an hour behind where the drill bit is. We have MWD units, LWD units. We’ve got wired pipe. 

We’ve got some of the capacity to move the data, but I don’t think we really have the capacity to use the data in a proactive fashion, really incorporating the data coming back so we can think ahead of the drill bit.

We’re trying to upgrade our capability managing higher and higher frequency multivariate data. If we’ve got six sensors, I don’t want to just use one. I’m going to use all six. There may be some sort of signal that comes, not just from one, but from a combination of several, so we want to do that. 

We’ve gotten pretty good at producing more oil, no doubt about that. But as shale producers have found out, they haven’t been doing all that well in producing more money and profitability, and they’ve sometimes had environmental issues.

We need to manage the whole, not the parts. We’ve come a long way in the last 30 years managing the parts. I think one of the challenges now is managing the whole.

When it comes to production or we’re dealing with the reservoir, the spatial analytics side becomes important.  We have SCADA data. We’ve got individual well production history; we’ve got all that. Now put that together in a cube. We’re not just dealing with the well, we’re dealing with a cube of rock, we’re building spatial understanding of the subsurface, and even on an operational side, energy use and emissions detection. How can I put all that together so that I am producing the field to make the most money, not just producing the field to make the most fluid volumes? 

BE: There are two other issues that I think need to be worked upon. There’s a lot of the sensors on a drilling rig that are not that accurate or not that precise. They’re not calibrated very often. You’ve got to have good information coming in to be able to come up with good insights, so improved sensors on drilling rigs is a factor as well as the data transmission. There’s wired pipe, but it’s very expensive and it has challenges in and of itself. 

Are there ways that we can get data from downhole back to us in a timely fashion at a rate we need right now? I don’t think we’re there. If we’re going to improve drilling operations, we need to have the information coming from the source, which is the drill bit, and the area around the drill bit, and we have to be able to deal with the velocity and the volume of data in real time so we can make decisions in real time. It doesn’t do you any good to know the well blew out and you’re on fire back there already. We need to know what’s happening now.

Have people been skeptical about incorporating data and analytics into the field? How have you dealt with it?

JC: The oil industry has been criticized, probably correctly, for being relatively slow adopters of some of this technology. My generation didn’t believe in the models enough. I think the new generation believes in them too much. We have to find somewhere in between. 

I don’t care if you are the slickest Python programmer in the world and you just built this reservoir model. You have to be able to explain it. 

Building trust is understanding your data and being able to explain it. It’s the physics as well as the data-driven analytical processes. It’s not one or the other, it’s both, and that’s a harder challenge.

BE: One of the things I like to tell our students in classes about the use of technology and information is you have to get buy-in from everybody, including in the field, because if the rig crew doesn’t want something to work, it won’t work. You’ve got to be able to sell your ideas, to explain what’s going on and why it’s going to make their job better and make their life easier. People are more willing to do stuff that helps them do their job better, and that’s how you have to sell it.

Are there any books, sites, or other resources you would recommend?

BE: Jim, this is a good time to talk about your two books!

JC: I have written two non-academic books: The Future Belongs to the Digital Engineer and A Digital Journey: The Transformation of the Oil and Gas IndustryI also blog on LinkedIn.

Automation will get rid of some jobs, probably jobs human beings don’t really want to deal with because they’re dangerous and dirty. The petroleum industry will certainly change, but it won’t go away. You’re going to have to become model masters and prediction wizards and future tellers and a whole bunch of funny things that you maybe didn’t get in your sophomore and junior classes in Petroleum Engineering. The role will change. I don’t think the role goes away, but if you don’t change with it, you might go away if your skill set isn’t competitive in the industry.

There’s going to be a greater emphasis on predicting what is going to happen and new ways of creating value, and with all of that, you need the data. I think it’s now inescapable that digital literacy is becoming a core competency of engineers, regardless of what specialty they go for, what industry they work in. AI is going to be a tool in the future. It’s going to be a co-worker and that’s something we have to wrap our heads around.

BE: I can add that other great resources include the different conferences, like the IADC Drilling Conference which had a number of sessions on digital transformation, and then also I’d recommend your local SPE. That’s a really great place just to get in on the ground floor about what’s going on and what your peers are doing in your region. 

Dr. Bill Eustes is an associate professor within the Petroleum Engineering Department at the Colorado School of Mines. He has a B.S. degree in Mechanical Engineering from Louisiana Tech University (1978), a M.S. Degree in Mechanical Engineering from the University of Colorado in Boulder (1989), and a Ph.D. in Petroleum Engineering from the Colorado School of Mines (1996). He specializes in drilling operations, experimental, and modeling research. 
Jim Crompton is a Professor of Practice at Colorado School of Mines. Jim retired from Chevron in 2013 after almost 37 years with the major international oil & gas company. After moving from Houston to Colorado Springs, Colorado, Jim established the Reflections Data Consulting LLC to continue his work in the area of data management and analytics for Exploration and Production industry.
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Data Science & Analytics Geology & Geoscience Passion for Change

Passion for Change: Diamondback Energy

with David Cannon, Senior Vice President of Geoscience and Technology at Diamondback Energy

The views expressed here are not the official views of Diamondback Energy, but those solely of the subject of this interview.

If you missed the first part in this series, you can find the start of our conversation here.

What do you see as some of the key technologies that are going to help you succeed in this price environment?

There’s a lot of very cost-intensive technologies out there. During good times in the oil and gas industry, people go out there and spend millions and millions of dollars collecting high-resolution data: microseismic surveys, wide azimuth seismic shoots, pilot holes, logs in the laterals, and so on. This data comes at an extremely high cost and can give you very detailed data from their collection, but during times such as these, they’re usually the first items cut, because they’re considered discretionary spending. To follow the theme from my first post, the social proof is no longer there. So during poor price environments, we must fall back upon our scientific understand and dust of our mental models to contextualize all that high-end data we collected during the good times.

Pilot projects, high-density data pad projects, a lot of those wellbore centric data collection efforts are really just that—wellbore centric. What does that tell us about a complex reservoir 2 miles away? It doesn’t really tell us much. It’s hard to take that high-resolution data in a very finite area and extrapolate that to other parts of the reservoir without knowing context. 

Geologists utilize many mental models. That’s what we do day in, day out. Sequence stratigraphy is a mental model. Structural geology concepts are mental models. Depositional environments are mental models, and we have to take all those data sources that we’ve collected and put them within that mental model. This is a key step in our progression toward data analytics and machine learning.

In data analytics and machine learning, the algorithms need context. You can’t just throw numbers into a black box and expect results to appear that make any physical sense, so you need to contextualize these things, that is where I’ve seen successes recently. Data science and machine learning are getting better with the contextualization. That has been my biggest issue with data science in the past. Very early on, it was very black boxy. You threw your data in the box, never to be seen again, then a result came out. Well, how did that work?

At Diamondback, we’re very curious about that workflow and critically interrogate whoever we partner with that provides these services. We want to see how it works. We want to be able to understand the entire process from data loading to result creation, because if we can’t explain it physically, then there’s no point acquiring it. It’s just a number, it’s just a result at that point, completely unknown with no uncertainty bounds whatsoever. How can I put any trust in that value if I don’t understand it? That’s where a lot of advancements have been had, and I think will continue to be had, in 2020 and beyond within this particular industry—pushing geologic contextualization within data science constructs.

What successes have you seen with data science or machine learning?

At Diamondback in particular, I think we’ve run the gamut. We’ve done some projects around machine learning early on, back in 2016. One of the questions we wanted to answer was: 

Here’s a whole mess of data on horizontal wells—gamma ray profiles, stimulation designs, post job reports, drilling data. Here’s all the geological information, what formation they’re in, all this other stuff. And basically, they smooshed it all together and they spit out a couple of answers for what drives production. Those two things? Better production werefrom wells that were toe up and had little undulation. So does the subsurface play a role at all in this? And when we were interrogating the provider on that, they said, we don’t have enough data”.

What does that mean? We gave them 300+ wells to utilize. But it turns out that’s not nearly enough data. We needed an order of magnitude more. In that particular example, what really enlightened us was the effect of data population. In order for an algorithm to properly learn through data, you need a ton of it.

If you don’t have a lot of data, the learning band that algorithm has is very, very narrow. You do not create an environment for that algorithm to adapt to new inputs, to outlier inputs. If you have a population of 200 wells and you have knowledge that 20 of them have outlier type production, that’s 10% of the population. Those outlier results start becoming part of the distribution. That’s a problem. 

One of the things we see as a success with our partnership with Petro.ai is proper contextualization of a data science project. Tapping into geoscience expertise to build better contextualization, in addition with more data, will result in better outputs. Outputs that we can trust in our business decisions of directing a multi-billion dollar capital budget. 

It seems like Diamondback has had success with data trades. Do you see that as a trend in the industry or is that sort of unique to what you guys are doing?

No one outside of the majors has the type or volume of data necessary to make a machine learning algorithm output results that are meaningful. The majors have thousands and thousands of wells, which is why they have whole departments dedicated to data science and machine learning for their own internal use. There’s a reason why, a lot of times, they don’t contract vendors because they have their own data scientists building the algorithms themselves and using them within the mental models at Exxon or Chevron. 

For people like us, we have to rely upon outside data sources, either through data trades or incorporating other means of information, and that’s one of the things we’ve been able to do very successfully, acting upon those data trades and squeeze a ton of value out of that data.

One of the justifications I’ve used with management teams for high-resolution data is how this expense can be scaled to how many trades I can make with the data. If it costs $1 million dollars to collect and analyze a core, it will theoretically cost $167,000 because I will trade this data for 5 others cores nearby with competitors. That in turn will allow us to gain more knowledge on our reservoirs without increasing capital cost. 

For our industry to continue to move forward as the preeminent source of energy, either stand alone or in conjunction with other sustainable energy sources, we all have to work together. Working with others with different perspectives keeps our innovative skills sharp. If you are insular, you fall into complacency. 

We have built a system that’s competitive through public trading of companies. We want to compete with Apache, Concho, and Pioneer, but the problem is, we’re all extracting the same commodity. It’s not like my oil is any different than Pioneer’s oil and it’s not like the end game for that particular barrel of oil is any different either. It’s still going to get refined into the same suite of products that we all use and enjoy in our modern society. 

We need to start combining our efforts to focus on more industry-friendly consortiums that allow us to share not just data, but share ideas. That’s really what it comes down to at the end of the day. If your rock is good, your production is good, full stop, but more efficient ways of extracting could potentially lift areas that maybe have marginal rock to make it economic. That is the power collaboration. We should all be working together to continue our ability to provide energy to our world to lift up the human experience.

Are there any good books or blogs you would recommend?

I’m a huge fan of social psychology; or the study of human interaction. Also, I am a fan of how human interaction and environmental interaction creates the basis for all cultures and social constructs. What are the things that you can that you can learn from interacting with humans that can provide a better basis of doing business with each other? There are a couple authors that I really enjoy reading books from. One is Yuval Noah Harari. He wrote one called Sapiens: A Brief History of Humankind. It’s a real interesting read about how our species evolved and got to the ‘top of the pyramid’. He’s written a few others. One of them is Homo Deus, which is about how our technological adaptation and our ability to communicate on a much grander scale now because of the proliferation of information. This has set into process an evolutionary change within humans that could be permanent. And as we move forward, he believes, this is the time where we may be able to classify ourselves as a species as something different from Homo sapiens in terms of our ability to coordinate with each other and how modern stimuli have changed us mentally and physically.

The other one I’m reading right now is 21 lessons for the 21st century. It’s a very good book. I’m about halfway through it right now. Lot of interesting topical things in there that contextualize modern issues.  The other author I really like is Malcolm Gladwell. He’s a journalist by profession, but he’s a fan of investigating how people interact and push ideas through society. He’s a firm believer of outliers, usually the very few within our society that push a majority of our advancements and ideas through society.

How would you describe Petro.ai?

Petro.ai provides a differential product that allows our company to have an edge on understanding what we’re doing in the subsurface and how we interact with it.

As we were selling this project internally, I think what differentiates you from other people, is the fact that, simply put, you provide the platform and access to expertise to allow us to thrive within your platform. And that is something that is differential. There’s a lot of people who push machine learning, who push their own platforms for being able to come to terms with how we interact with the subsurface, but it’s usually all project-based. They don’t give you the keys. It’s like an Uber. You have to call them and they show up and they pick you up, whereas you guys are selling us the car. We’re able to drive it ourselves, but then we go back to the dealership every so often: something’s wrong here and you help us fix it, but we still own the car at the end of the day. So that’s kind of how I view Petro.ai.

That’s the kind of partnership we want, as I said before, we want to understand. We want to be able to know where to fix the issue if something breaks down. That is important to us because it allows our organization to learn and adapt in the proper manner to new information. 

David Cannon has served as Senior Vice President of Geoscience and Technology since February 2019. Previously, he served as Vice President of Geoscience from April 2017 to February 2019, Exploration Manager from March 2015 to April 2017, and as a Senior Geologist since March 2014. Before joining Diamondback, Mr. Cannon served as a Senior Geologist for Newfield Exploration, from January 2013 to March 2014, where he assisted in exploration, assessment, and development of SCOOP/STACK properties in the Anadarko Basin. Prior to that, he held the position of District Geologist for Samson Resources, from August 2011 to January 2013, where he held a position in the Corporate Exploration Department assessing Rockies, Mid-Continent, and East Texas unconventional plays. He was recruited by ConocoPhillips in 2008, where he held various positions in exploration and development of Rockies and Bakken assets. Mr. Cannon received his BS Geology from the State University of New York, College at Brockport in 2005 and obtained his MS in Geoscience from Pennsylvania State University in 2008 with a focus on structural geology and rock mechanics.

Petro.ai uses AI and Machine Learning to empower domain experts, data scientists, and executives with instant information in the right context, energizing teams and transforming data into action. Partnerships with global leaders in cloud computing (AWS) and geoscience (Dr. Mark Zoback) allow Petro.ai to deliver differentiated technology and teams to Accelerate Discovery.

Categories
Data Science & Analytics Geology & Geoscience Passion for Change

Passion for Change: Diamondback Energy

with David Cannon, Senior Vice President of Geoscience and Technology at Diamondback Energy

The views expressed here are not the official views of Diamondback Energy, but those solely of the subject of this interview.

Tell us about your background and what you do now.

I’m the Senior Vice President of Geoscience and Technology at Diamondback. Mostly my purview is on the subsurface side—being able to better characterize and extract Diamondback’s resources in a cost-efficient manner.

I started out in the industry back in 2007, a geoscientist by trade and by formal education. I got my Master’s Degree at Penn State University in Structural Geology with a focus mostly on rock mechanics and fracture mechanics, so I’m one of those weird geologists that actually likes math.

In our industry history, geoscientists have always worn the mantle as being those who spend all the money without regard for economics, under the guise of pursuit for truth. As I moved into the oil and gas industry, I think I’ve always had an inherent businessmen’s mind, which resulted in marrying science and technology of the subsurface into sound business decisions.

I know there’s a lot of others that think this way, too. Can we marry the pursuit for data and truth in the subsurface with sound economic practice? We can do these things in a cost-efficient manner that not only allows us to succeed in terms of creating a business case for data collection or data manipulation— which is where I see players like Petro.ai being an integral part of—but that also allows us to get stakeholders in the process. 

Engineers tend to have an economic mindset:  what’s the most cost-effective practice to be able to maximize my return? And if you can sing along those same lines, I think that you can get a lot of buy-in on new technologies, a lot of buy-in on something that might be disruptive for your particular industry. That’s honestly the way all companies should approach it. 

We have to stop thinking that this is an antagonistic setting where the geologists have one way of thinking, engineers another, and the finance guys another. We all have to meld our thought processes together to be able to get the best outcomes.

Why do you have a passion for change in this industry?

Our industry should always have a passion for change.  The way I see it is we must always evolve, and if we do not evolve, our industry will perish. It’s borne out in the data. You see historical references to it all the time. 

One particular industry that has gone through a period of low innovation, and now feels the repercussions, is the coal industry; one of the preeminent sources of energy for the world from the 1800s through I think about the 1980s. Well, they got complacent. That complacency led to a slowdown in innovation around things like more efficient extraction techniques and more efficient, environmentally friendly conversion techniques. Are there ways to thermally alter coal in a way that can reduce the amount of greenhouse gases and reduce the amount of pollution that is released into our environment? I just don’t think they were ever ahead on that respect. 

They always reacted whenever new regulations came out or whenever there was social pressure. Only then would the coal industry react, and they usually reacted in a very minimal way, just enough to get by. They were on top. Coal would reign forever. Why would they have to change? 

Technology evolves as new social pressures and new paradigm shifts occur throughout our human society. Other alternative sources for that energy started to come to the forefront, and I think the biggest displacement technology we see now is just switching over to natural gas. It’s simple, right? 

You can take a coal plant and convert it to a natural gas plant relatively easily, and with the advent of horizontal drilling and hydraulic stimulation in our industry, this resulted in an oversupply of natural gas. That fuel source became extremely cheap, so all the utilities that ran these coal fire plants saw an opportunity to behave in a much more efficient manner. At the same time, gas was able to better answer the calls for change from environmentalists and from general social pressure around pollution. So, they made the switch. 

More and more coal plants are continuing to switch to natural gas even today, and the market share of coal continues to slide. They are no longer the number one producer of electricity in the world. They’ve been displaced and they will continue to be displaced. The only reason why they were displaced isn’t really because natural gas is just better. It’s because they stopped innovating. They stopped seeing where they fit within the future and seeing how they can adapt to that new reality. And that is a problem that a lot of industries have, and that’s something I’m passionate about with our industry, as I can see us traveling down that same road really easily. 

If we continue to be complacent about where we fall within the energy industry and overall giving energy to the human population, we can easily be displaced if we’re not thinking about new technologies. 

There’s some research that’s being done now with the Earth and Mineral Sciences Department at Penn State in conjunction with private industry in Appalachia, looking at ways to take natural gas methane streams and thermally alter them with microwave plasma. The process breaks down methane and converts it into molecular hydrogen, for hydrogen fuel cell technologies, and graphene for structural additives to steel and concrete. So, they’re thinking about how our industry, the hydrocarbon industry, can be a part of the solution of renewable energy.

It’s not an or statement. It’s an and statement. And that’s one of the things that I’m passionate about is that our industry can be a part of that solution. We just have hurdles, and it’s mostly philosophical hurdles, of people saying, “we’ve always done it this way. We’ve always provided energy this way. This is how we’re going to do it.”

That is the mentality that kills you. That’s exactly what happened to the coal industry in the 60s and the 70s, and they refused to change because of that mindset. They basically said, “where else will you get your energy?” Well, they found out where else the world could get their energy. So, we can’t stand by and let that happen. It will be the death knell of our industry if we don’t find ways to couple into the new research around providing energy in a more sustainable way to the world.

It’s a full cycle problem. It’s not just extraction. Extraction is one part of the problem. It’s also taking that product, then converting it to the energy that’s consumable for whoever your consumer base is. That’s one of the things that I think we have an issue with is that we’re always really decoupled in that.

The independent E&P companies, they’re just worried about extraction and production, right? Once they sell the product, you’ve ended the value proposition. End of story. Move on to the next barrel. But some of the engineering, some of the science, some of the application of technologies that we’re using in the extraction realm could also help on the consumption realm, because at the end of the day, the physics, chemistry, biology that we use on a daily basis wraps up into the work done to convert that resource to energy. 

Are we going to completely focus on the science of resource conversion? No, because we have our business models that state that we have to extract this resource. But are there ways that we can bring ideas to the forefront that we could change the game on how those resources are then used? Yes, and we as extraction companies should be part of that research and ultimately that solution.

In 2015 and 2016, the downturn gave rise to this digital transformation in oil and gas. How do you see this 2020 downturn affecting things?

I always fall back upon is the diffusion of innovation. It’s a concept that was actually written back in the early 60s talking about how technology or even an idea, moves through and is adopted by a population. It’s an elegant construct really.

When a particular idea or technology starts, you have the innovators, the ones who are creating that process, the ones who are getting the bloody nose because they’re the first ones through the wall. Then you have early adopters, folks who say, “That’s a really cool idea. I want to adopt it and potentially make it better. Let’s push this technology forward.”

One perfect example is Elon Musk. Plug-in electric vehicles have been around since the 1960s. They actually made functional models of plug-in electric vehicles back then, so what he’s been able to deliver with the Tesla vehicle is nothing new. He was an early adopter of that technology. We have better complementary technology around battery tech and energy efficiency to increase range and operate more technologically advanced vehicles. So, he’s an early adopter. I wouldn’t consider him an innovator. 

Then once you go past the early adopter phase, you have what’s called the technological chasm. It’s at that point where the technology has to reach some sort of social proof, and social proof can be defined by anything, depending on the social system that you’re trying to push an innovation or an idea through. That social proof can change and vary, dependent upon the answer and solution that technology is trying to address. 

To continue with the example of electric vehicles, it’s usually cost and value. A middle-class family of four in Iowa wants not only a vehicle that has a long-extended range, which Tesla has, but they also want a vehicle that has a low cost. When that family buys a Toyota Camry for $30,000, they get the benefits of long range and value. This is why the Tesla Model S hasn’t jumped the chasm, because that vast consumer base, middle-class America, cannot couple range AND value. That’s why electric vehicles have not jumped the chasm, because right now they have not attained the social proof of value.

In our industry, oil and gas, the social proof for technological innovation is also monetary. It’s essentially the price of oil. If the price of oil drops below a certain threshold level a lot of technological advancements can’t jump the chasm to get more adaptation through our industry. 

What we do, what we spend money on, is inherently tied to the price of the product we sell. How much money we make, how much revenue we make on a quarter over quarter basis, year over year basis, is going to be our war chest to be able to go out and spend dollars on innovation and potentially be more efficient. 

During times like 2015, 2016, and also today, are times where the social proof concept falls apart for our industry, and a lot of innovations fall backwards in that curve. They no longer have social proof, and people start to drop that technology because it’s no longer viable within that social proof context.

The interesting part about that entire thesis from E.M. Rogers around technological adaptations is when you have something that falls out of social acceptance, something else replaces it. There’s always something waiting in the wings to get social proof in times like this. The constant struggle with competing technologies as social proof shifts and changes with time causes a very stilted technological history within our industry. When times are good, we focus innovation on subsurface assessment. When times are poor, we focus innovation on operational efficiency.  

Things like data science and machine learning met social proof in 2015 and 2016, as they were disruptors on the efficiency side. Then as things started getting better, there was a stasis, a plateauing of data science. There wasn’t this huge rush of, “Let’s keep pushing the paradigm and pushing the technology for our industry.” Instead, we fell into, “Well, this works, let’s just keep using it.” I predict we will see another creaming event for those technologies in 2020 and beyond, because I think the learnings that were acquired in 2015 and 2016 are going to be brought back up to the forefront and the value propositions are going to be shown again. Then there’s going to be more people paying attention to the technology. In turn, that attention is then going to continue to evolve it, and that evolution then keeps that technology moving forward and doesn’t allow it to get complacent and lag like the coal industry did.

The conversation continues here.

David Cannon has served as Senior Vice President of Geoscience and Technology since February 2019. Previously, he served as Vice President of Geoscience from April 2017 to February 2019, Exploration Manager from March 2015 to April 2017, and as a Senior Geologist since March 2014. Before joining Diamondback, Mr. Cannon served as a Senior Geologist for Newfield Exploration, from January 2013 to March 2014, where he assisted in exploration, assessment, and development of SCOOP/STACK properties in the Anadarko Basin. Prior to that, he held the position of District Geologist for Samson Resources, from August 2011 to January 2013, where he held a position in the Corporate Exploration Department assessing Rockies, Mid-Continent, and East Texas unconventional plays. He was recruited by ConocoPhillips in 2008, where he held various positions in exploration and development of Rockies and Bakken assets. Mr. Cannon received his BS Geology from the State University of New York, College at Brockport in 2005 and obtained his MS in Geoscience from Pennsylvania State University in 2008 with a focus on structural geology and rock mechanics.