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Data Science & Analytics Passion for Change

Passion for Change: Rife Resources

with Bob Lamond, VP Asset Development at Rife Resources Ltd. 

Tell us about your background and what you do now.

I started my career as a technical geologist, educated in Ontario, Canada, then went to grad school for Paleontology at the University of Utah. In the middle of studying trace fossils in Utah and Kenya, I was recruited by Exxon & ultimately did not complete my Master’s degree. Initially, I had no desire to work in oil and gas – I wanted to be a paleontologist! I really fell in love with the huge datasets, the incredibly bright people, and the massive, global projects that Exxon was working on. And so, with stars in my eyes, I returned to Calgary to begin working at Imperial Oil.

At Imperial, I was first exposed to geologic modeling, where you develop three-dimensional computer models of the subsurface geology that allow us to test development plans and production strategies prior to drilling. That’s when I started getting heavily exposed to the computing side of geology and began to see the promise of a new way.

After a few years, I left Imperial Oil and went to Shell Canada for about five years – working almost purely as a geologic modeler, building models (a lot of geostatistics) and trying to incorporate those into our development plans in Canadian heavy oil and tight gas assets.

Following this, I joined Murphy Oil, working both here in Canada and in Houston. For some reason, they gave me – a computer modeling geologist – the opportunity to manage a subsurface team. At the time, a career in management was something I absolutely did not want to do. It has turned out to be the best (work-related) gift I’ve ever been given!

Two and a half years ago, I joined this amazing little company – Rife Resources. Rife Resources is a private exploration and production company in Western Canada that also manages the assets of Canpar Holdings and Freehold Royalties. 

My current title is a little unusual—VP of Asset Development. My job has been to focus on geology and subsurface engineering, but also on the innovation and analytic side for the company. At Rife, we traditionally do small ‘e’ exploration, we do geologic mapping, lease land, we plan and drill wells, we operate our fields responsibly. But what we’re really trying to do to stand out is to be creative in the fields where we’re working. These areas have been seen drilling for over 50 years and thus everyonealready knows everything about them. Well, you know, it turns out not everything! With our humble little company, in our humble old oil fields, we’ve recently drilled some consistently amazing & top-producing wells just from applying good technology mixed with traditional geology and engineering.

On the Freehold and Canpar side, we manage millions of acres of land, thousands of different leases, with thousands of wells that pay us royalties on a month-by-month basis. It’s a phenomenally rich data set, especially at a small company, for applying analytics.

Because these companies have been around for a few decades here in Canada, they’ve previously had a very traditional way of doing this work; paper and manual effort, numerous spreadsheets, phone calls, and so on …. until now. What we really endeavored to do over the last few years is to streamline, modernize, and finally be able to better track our assets. This very much appeals to my data-loving side.

We have seen enough early success that we have even started a small business unit we call “Analytics and Innovation”. This small team is being managed by another semi-ex geologist, Shayne Chidlaw, and is helping now to support projects across all disciplines in all three companies.

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

I have a real passion for streamlining and modernizing our industry. We’ve relied on paper and multiple Excel projects and the ‘gut feel’ way of doing things too long. Doing things the way we’ve always done them—we gotten stuck maybe a decade behind where we should have been, and now we must spend the money & effort to do this work and the analytics properly. 

You really don’t need a ton of money to get started in this work. You need access to data. You need some people to help compile & sort it, get it in the right place. Then, you just need some creative people, demonstrate a new way of working and spur them to make magic happen. When you have a good project that works, it breeds curiosity and excitement in others, and really gets teams going. 

My absolute work passion revolves around managing people. I find management most effective when you focus on people’s enjoyment and their fulfillment at work—if they love what they’re doing, if they feel the tasks they’re working on are important, and if they work for people who are highly interested in what they’re doing. They come to work charged up. They bring their best ideas. They bring their best work every day. So where does analytics fit into that? It allows people to show their creativity. 

It’s no fun punching through a whole bunch of paper spending all day trying to compile data. It is hard to be creative with the few minutes left to work after you spent all day data gathering. What is fun is to have a dynamic dashboard where you can quickly and confidently dig through information and tools you can modify to effectively get meaningful things done.

Do you think this 2020 downturn will speed up the transformation or slow things down?

I think it could go either way. It really should drive innovation because people are going to have to begin doing more with less. Unfortunately, when conditions are really poor, it leads management not to want to pay for the workforce or the data that they would need to do that.

As a company, we are asking ourselves, “Are we in survival mode, maintain mode or thrive mode? Do we just want to limp along, so that at the end of this, the last person can turn off the lights? Do we want to maintain? So at the other end of this downtrend, we come out the same as before. Or, do we want to use this time to come through this stronger, more nimble, with more people using technology?”

Another way of looking at this is that there will be no new energy company that will start up without a data scientist, or at least an eye on starting with lean, efficient data processes. That’s going to be the transition. Too early to know the details, but all new companies are going to be structured fundamentally differently than the old ones. We’re going to need to be the mammals on the other side of this extinction.

What successes have you seen in data science, machine learning, or AI?

I do love the idea of using machine learning to explore and find insights about physical reality, but then rerunning the whole process iteratively, where the machine is informing the human and the humans are informing the machine, and repeating. It’s like a co-learning process. 

Using data science and AI—you can get an answer every time, but is that answer actually grounded in reality or physics and thus will it be useful to predict the future?

Seismic data makes one of the best examples. Geophysics has been far ahead of the industry and have been using computer guided technology for a long time.

When you move into applications like AI for automatic legal document translation, or take lease documents and attempt to apply machine learning/AI to interpret detailed clauses, you naturally generate lots of skepticism in land and legal colleagues!

So, how do you get people to have an open mind, give the technology try and then potentially embrace it? I see more of a human struggle than a computer struggle right now.

How have you seen other competitors start to adopt data science or analytics?

Personally, I prefer being a bit more on the leading edge versus letting others work the bugs out before we try it. It’s a lot more fun, but not always more effective. Other companies (including us) are doing amazing things right now, and it makes the most sense for us to learn from others and be a “fast follower” on most technologies.

We all benefit from people sharing open data, open processes. Companies like Petro.ai who work with multiple different companies—the learnings build up and become massively valuable for companies like us—we can’t afford to be on the cutting edge of too many things. No other company has the same issues and problems we have, and we’re not at the size or scale that makes any sense for us to be the innovators of everything. 

I have made it a policy for myself my team to share as much as we are able with industry, with companies like Petro.ai, with really any interested party, not just because we will ultimately benefit, but that the whole industry stands to benefit from the sharing of these things.

A small but current example of this: in the meeting I had just before this interview, one of our production engineers – who could just have been just doing his ‘job’, generating Excel sheets & reports day after day, took the time and initiative to innovate, taught himself how to  pull data from a few of our databases, built his own dashboard, and then shared it with everyone. This initiative is going to be both useful and contagious!

Being at a small company like us, we are nimble enough to be a hotbed for innovation. We just won’t have the funding to make it a bigger scale type project. That’s where we rely on companies like Petro.ai to help us out, where you guys have the ability, the skill, and technology, and you see what other companies are doing. 

Would you suggest any good books or blogs that you’re reading?

One of my favorites by Nassim Nicholas Taleb, who wrote Antifragile and The Black Swan, is Fooled by Randomness. I’ve been pushing this antifragile mentality within the company and should be considered as essential reading during this downturn. Another book about analytical thinking that I really enjoyed is by Jordan Ellenberg, How Not to Be Wrong: The Power of Mathematical Thinking.

I have also reread the classic Zen and the Art of Motorcycle Maintenance by Robert M. Pirsig. While definitely dated, it contains some beautiful parallels with our current battles of the romance of new technology with the nuts and bolts of older reliable tech. Mostly unrelated, I am rebuilding an older titanium road bike in my spare time, when I get away from my computer.

A podcast I always find soothing and wonderful is This American Life with Ira Glass. It’s just a great way to think about other things and other people and puts you in a good headspace every time. 

Do you think there’s a difference between the way you guys in Canada are viewing this pandemic and this market versus some of your peers in the United States?

Canadians have been trying to work within cash flow and dealing with lower commodity prices for multiple years now. Most people here have been dealing with this reality for quite some time. I think this creates less of an acute feeling of despair despite how bad the market is currently. A survivor mentality is well-entrenched in Calgary. 

On the flip side though, no one seems to bounce back like you guys do. Americans may seem to be in a steeper trough at the moment, but I predict that you will rebound and climb out of this a lot faster than us up north. Our sine wave is just a little more muted than yours.

In your own words, how would you describe what Petro.ai is?

I love the promise of being able to use our data in a flexible tool environment. I have a problem with ‘brittle’ software that says: “this is how you want to look at the data, this is how you want to work the data, this is what your graphs should look like, this is what you get to do.” 

With Petro.ai, we want to look at our data in our own way and designed specifically for the needs of our own companies. It allows us to build projects that we’re really not able to build ourselves in a shorter time than we could have built it. 

Like everyone, we are facing budget reductions everywhere in our company. However, when we brought up continuing with Petro.ai, we still said yes. This is a really important project for us, and success here will allow us to emerge in a stronger, thriving and antifragile state on the other side of this downturn. 

 Bob Lamond is a creative, dynamic, and caring subsurface manager with >20 years of diverse unconventional development and production geology background. 
 
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Passion for Change

Passion for Change: Geoffrey Cann

with Richard Gaut, CFO & COO of Petro.ai and Geoffrey Cann, Speaker, Trainer, and Author of Bits, Bytes, and Barrels: The Digital Transformation of Oil and Gas

Watch Now.


Full Transcript

Richard Gaut: Geoff, thanks for joining us on our Passion for Change interview series! You’ve written a really influential book, Bits, Bytes, and Barrels: The Digital Transformation of Oil and Gas, that our customers have been talking about, that’s getting a lot of publicity, and that is part of the zeitgeist now. It is so great to have you on as a guest for our very first video interview. 

Geoffrey Cann: I very much appreciate the invite. Thank you so much. 

RG: Across the oil and gas complex, what do you see as the technology with the largest opportunity for a digital solution to make an impact?

GC: The digital solution which offers the greatest potential today – by far – is the world of artificial intelligence and machine learning. The oil and gas industry is blessed with enormous deposits of data assets, which have accumulated over the years and will continue to accumulate. 

Unfortunately, this data sits in places where the industry has either forgotten it exists, doesn’t understand its value, or dismisses it out of hand as being “dirty data”. I believe that the fastest way to value in the world of digital isn’t necessarily to generate more data, though that’s very easy to do. Instead, it is harvesting the data assets that you’ve already got. If you wanted a shot that you could pull today that would yield a meaningful outcome, it would be to apply artificial intelligence or machine learning somewhere in the business.

RG: Where do you see folks on this transition from managing their own IT systems versus finding service providers in the cloud? What’s the industry doing to manage these huge data volumes?

GC: Well, the first challenge that the industry has to come to grips with is, as you point out, is the enormous growth in the volume of data out there. There’s the first problem. How do you get your arms around all of this data and, if you’re an oil company, can you afford to stand up your own incremental infrastructure year-on-year, just to store all of this data?

That brings with it all kinds of other interesting questions: Where do you locate your data center? How do you handle backups and recoveries? How do you build in your redundancies? What about your redundant power supplies? How are you going to even fuel it, since so much energy goes into running a data center.

The leading oil and gas companies have concluded that the right answer is to shift off of this “roll your own” infrastructure and to leverage the capabilities afforded by the large cloud computing companies. Migrating out of your proprietary data center and onto cloud infrastructure. That, in turn, opens up all of the new business model possibilities that we’ve seen from other industries that have migrated ahead of oil and gas. That’s step one.

Step two, though, has to be investing in the talent and the capability to take the data that you’re sitting on and make sense of it. That’s where the need to bring onboard data scientists and other data specializations comes from; so that you can begin to extract the value promise from all of that data.

RG: There’s a really great phrase in the book that I learned and I hadn’t heard this one before, Geoff. It’s “wetware.” Can you tell me what “wetware” is?

GC: Well, if software is what’s on your computer and hardware is an iPhone, then wetware is you and me. We are our own compute capacity. It’s just up here in your brain where things are wet! So wetware refers to the humans that are working with both hardware and software. For the time being, we are going to be in a wetware world. We’re going to have lots of people managing and administering our facilities and our assets. 

RG: The fact the matter is that humans just weren’t designed for it. Wetware just is not capable of digesting, ingesting, or contextualizing these incredible volumes of data that we’re now privy to. 

GC: Quite right. As humans, we learn at a certain pace and so we are at a significant disadvantage when you think about the pace of digital change in how fast machines are able to learn.

RG: It feels like there’s some top-down initiatives at the board level to undertake some of these transformation initiatives, but when the rubber meets the road inside the company things are more challenging. What have been that the successful strategies that companies have undertaken to take a tangible first step after that memo comes down from the board? 

GC: The short and quick path forward that most companies take is that they will create some kind of digital task force, innovation council, or digital Center of Excellence somewhere in their organization. Then it becomes this group’s job to move digital initiatives forward. This can work, but in my view it needs four essential ingredients for success. One, it needs organization. Two, it needs to have resources so it can actually do things: money and budget to spend. Number three is that it needs to have ways of working. Fourth is that team needs to have real hard measures of success. If you don’t have those four ingredients, your task force is not going to be successful.

The second ingredient you have to have in place is it’s got to be implemented in a business unit. To get to a successful outcome, the business unit itself has to be ready to embrace this digital change. That means changing the performance metrics for the manager in that unit. Then, you need to train the workforce in that unit so that they know that what’s coming at them is an expectation of the company. If the workforce doesn’t embrace these changes and drive digital growth, then the whole unit will suffer.

RG: You make a really interesting argument about what competes for capital in an up market versus what competes for capital in a down Market. Would love to hear your specific thoughts about that.

GC: We have some real challenges in the context of how to drive this change agenda forward. You can go from midstream companies with a viable digital game plans underway, to upstream companies, and even to refineries. The place in the value chain doesn’t matter; the digital agenda should continue to run regardless of where we are in the cycle. 

RG: Another thing that I was really interested in was IT and OT and their roles in digital transformation. If you could just walk us through how they end up managing these projects.

GC: Sure. Most commercial businesses will have an Information Technology (IT) department and within it you’ll find the team that makes sure the email system works correctly, the ERP systems are supported properly, and that the infrastructure is in place to do things like Zoom calls. They let you bring your tablet to work and gives you single sign-on and all that sort of stuff. IT’s specialization is integrating these multiple technologies together and making them appear seamless. That’s one of their secret sauces. The are generally very good at patching, keeping complex systems going, and securing and providing a whole range of services responding to employee needs.

OT is what we call Operational Technology. OT is what you find in a plant as it runs 24/7. It never shuts down. It is responsible for keeping physical infrastructure running within certain set points. OT can go by the name SCADA, which stands for Supervisory Control and Data Acquisition. Here, you’re supervising an asset and you’re capturing the data from that asset as it’s running. Historically, IT and OT have been two separate solitudes.

The problem, though, is that in a digital world, they start to come together. If you look at the oil and gas industries from one end of the spectrum to the other (upstream, midstream, downstream, retail, trading, or capital projects), you’ll find slight and distinct differences all the way along the chain. Differences in ghw people think about an approach the world of IT their world of Operations Technology and how they connect in the world of digital technology. There isn’t a clear cut answer emerging … yet.

RG: This has been really fantastic, Geoff! I greatly appreciate the opportunity to visit with you. I wanted to show the group that we have our own copy of Bits, Bytes, and Barrels that you were kind enough to help us print our own Petro.ai logo on. So, if this is something that you are interested in, follow us on LinkedIn and join our Petro.ai ommunity and we will give you an opportunity to get a copy of Geoff’s book. We’d love to share this with you. But, before we sign off is there any wisdom you’d want to share with us as parting words?

GC: Not one thing, but three things! The first is that I write a weekly article series about digital innovation in oil and gas which is available on my website. It’s absolutely free. A companion to that is a podcast that I also publish every week on iTunes, Stitcher, and Spotify and all the places where you find podcasts. It’s called Digital Oil and Gas. Third, a government agency asked me if I would turn my book into a training course and so I did that for them. I built all the materials and then recorded all the materials as a series of online lectures and they’re available on Udemy for about the same price as the book itself. 

RG: Thanks so much for taking the time.

GC: You bet. I’m delighted to do it and look forward to doing this sometime in the future again. Take care.

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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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Passion for Change

Passion for Change: Birchcliff Energy

with Theo van der Werken, Asset Manager at Birchcliff Energy

Tell us about your background and what you do now.

I am currently employed with Birchcliff Energy, which is a Canadian based intermediate producer with a large acreage position in the Montney unconventional resource play.  As the Asset Manager, I manage a team of multidisciplinary engineers and geoscientists that are very busy optimizing the development of our unconventional resource in the Montney.

I’m originally from Holland, where I graduated with a degree in Mining Engineering. Before graduating, I completed an internship, working offshore in the North Sea and realized that an oil and gas career path was more aligned with my interest. 

My first job took me to the Middle East, where I worked for an oil and gas service company. I was involved with a major that utilized Underbalanced Drilling as a drilling technology to explore for oil in the Omani desert. I was based out of Dubai, where I started my career in the drilling engineering department. Subsequently I relocated to Houston where after some good field experience in Texas and Canada, I pivoted from drilling into reservoir engineering.  

After several years I made the conscious decision to switch to the operator side and joined a large multinational and relocated from Houston to Calgary where I started working as an Exploitation engineer in an asset team. It was a great experience, I got to see a lot of different things and work on a variety of reservoirs. Subsequently, I went to a start-up and spent about two and a half years with them. This is where I picked up and learned a lot of the surface side of the business: production engineering, facilities, pipelines, joint venture and so forth.

I joined Birchcliff Energy in 2011 where I started as a Senior Development Engineer and took the role as Asset Manager at the end of 2011. I’ve been in this position for nine years, which has been very rewarding and I have never looked back. 

During the last 9 years we have seen tremendous growth in my team and in the company as whole, primarily through the drill bit where we have grown from approximately 16,000Boed to 80,000Boed. 

Can you tell us why you have a passion for change in this industry and what else you’re passionate about?

I think the oil and gas industry is a very exciting and dynamic industry that is under-appreciated as it relates to technological innovation. 

In addition, the industry often gets vilified by the general public without really understanding the disconnect between end-user habits and the associated energy requirements.  This motivates me to not just advocate for our industry, but also strive to continuously improve on the responsible extraction of hydrocarbons. 

In the last 20 years, with the rise of the unconventionals, we have seen a tremendous amount of technological innovation to both hardware and software that is used to extract hydrocarbons from tight oil and gas reservoirs. 

Above ground, rig automation has evolved with fit-for-purpose rig designs that are really well suited for large scale pad development. At Birchcliff, we are now developing our field with surface pads that can accommodate up to 28 wells from one surface location, minimizing the environmental footprint. These types of walking rigs are surprisingly agile and really help reduce cycle time and ultimately drive down finding and development cost. 

In the subsurface, we continue to see innovations on the software side with more advanced integrated physics-based models as well as data driven models that can guide completion design and field development strategies. In the field, the use of advanced diagnostics such as fiber, geophones and pressure data are really insightful to capture real time system behaviour as we zipper frac these massive pads. 

The diagnostic data is very useful to further advance the modelling space to calibrate and validate not just our hypothesis, but often also to test the reliability of these models. 

Because the system is so complex and the industry continues to innovate there is a great opportunity for continuous improvement on optimizing “where to drill, how to drill, where to frac and how to frac.” 

That’s my number one passion that I rally my team around—we have an opportunity to do better every year, or even every well, based on integrating more data sets, looking at new technology and just continuously pushing the envelope of how we develop these unconventional reservoirs.

As technology advances, it allows asset teams to move down the grain size, if you will. What was once viewed as poor quality rock back in 2011—we’ve now added multiple horizons. Technology is allowing us to economically explore, even with declining commodity prices and mounting external pressures and taxes.

To this point, tight reservoirs have revolutionized the supply side and it’s really driven down prices, particularly in natural gas sector. Notwithstanding that, we can still make a go of it with more room on the upside. That’s just really fascinating and motivating to me and my team. That’s the passion for what I do at work.

Outside of work, I would say my passion—and why I’m living in Canada as a Dutchman—is the mountains. I really like the outdoors, always have. Holland is a pretty busy place. There’s about 17 million people in the size of Southern Alberta, whereas all of Canada has about 38 million people. There is a ton of room here. It’s absolutely beautiful country in summer and winter. I really enjoy spending time with my friends and family in the mountains. I’m pretty passionate about that.

Do you think this downturn we’re experiencing now will accelerate digital transformation or put it on pause until we see better commodity prices?

I think we’re stalling a bit to be honest with you. It seems that we are all very rattled with everything that’s going on from biological viruses to this price war. In North America, there’s 35 billion dollars of capital that’s basically been pulled out of the 2020 budget plans.

On top of that, you layer on remote working and suddenly implementing a digital strategy becomes daunting. When I think about implementing a digital transformation, it’s really a management of change process that is quite culturally involved.

A successful digital transformation is a lot more than buying software. It’s a lot more than hiring a data scientist.  Making sure you assemble the right team and align with the right industry partner are all important components that need to be interlaced. Then building enough internal buy-in and getting people to culturally rally around that is very involved. 

If you’ve already started that journey, I think you can continue to reap the benefits and dig in on specific projects. But if you haven’t started yet, I don’t think this price shock alone gives you the push.

I feel very comfortable with what we are doing here at Birchcliff. We have detailed road maps and projects and inventory of things that we’re working on. We’ve got the people, the data engineering, and the data pipelines. I feel good about that. 

How much do you see your culture tying to your competitive advantage, being able to capture that next generation of knowledge?

It goes back to this passion for change, passion for continuous improvement. We’ve built an internal framework with some tangible tools. How can you continuously improve? We want to improve everything: from trialing different completion styles in the field, to new technology using physics-based and data-driven models, to spending time collaborating with peers, to competitor intelligence to learn from “best in class” competitors.

In addition, we have set up the business processes that support these efforts. How do you design a pilot or operational trial? How do you define success with appropriate KPIs? Who is responsible for scouting for new technology? All these items are important to maximize value. 

That interlacing of these various components is what is part of our Continuous Improvement framework. Data analytics and science is just one of the tools that fit within this framework. If our team decides to set up a field trial, we need the right sensors in the field so we can collect the right data to feed a data-driven model or calibrate our physics-based model. The culture of continuous improvement—and people rallying around that—allows us to get the buy-in for something like data analytics or an investment in physics-based modeling.

We just got approval to run downhole fiber in this environment. We’re making this investment because our people are bought in on how data, physics, and analytics interplay to drive continuous improvement. So, they all go hand-in-hand.

Some would argue that our Birchcliff sandbox is not the most competitive sandbox from a pressure and permeability perspective. If you look at the Montney, we’ve got a great position. It’s all contiguous land and we own our own plant and we’ve done a series of things which make our strategy highly successful and profitable compared to most of our peers.

Can you make a go of it without spending any money on modeling, field trials or diagnostics? If you have superior rock, you can probably still be very competitive. In the long run, I think the winners and survivors need to strive for this Continuous Improvement culture that is very much alive at Birchcliff. Our technical teams have been able to demonstrate that by year-over-year improvements on type curves and a variety of economic indicators.

Have you seen specific examples of success with data science and machine learning projects? And was there skepticism? How did you convince people to go along with it?

Analytics adds value in two places: 1) improving the efficiency of things that you already do and 2) by making better decisions because you’re able to interact with different data sets that you weren’t able to interface with before.

On the first point, we all want to be more efficient; that applies to everyone within Birchcliff. You can spend a lot of money moving data from external sources into your organization and then cleaning and staging it for an end product. The fact of the matter is, you don’t need to spend a lot of money to make some improvements on that. You just need some smart people and some software tools. 

If you can help make better decisions in terms of how you manage your production, that’s directly going to hit your revenue line. So, initially we focused a lot on production engineering with visualization dashboards. Those were some of the early use cases. By no means have we figured it all out, but we started small. Back in 2013, we organized data, then slowly but surely, we started to evaluate different tools. We started to build a network of people that we thought were like-minded and hired our first engineer with advanced analytics skills. 

That small group accelerated the adaptation of a lot of things. We slowly started to add people and showed more value in projects; that has led to where we are today. We have a dedicated data analytics team, which is rolled up under corporate development and competitor intelligence. We’ve since hired a data engineer and data scientist. 

That team didn’t make a lot of noise until we felt that it was worth bringing up with the broader organization. Many people, myself included, need “soak time” on these things. Having some tact around how you slowly but surely get your organization to adapt, there’s some strategy involved. For us, things have accelerated in a very positive direction.

Have you seen any competitors that are having success with data science and analytics? 

I think you see very different things depending on the size of the company. Larger players often employ highly technical and specialized people with great skill sets. But, individuals can feel isolated within the larger organizational chart. Some of those smart people are building some really cool analytics workflows, but they’re having a difficult time making it a broader initiative or socializing it to a larger group. 

On the other end, you see these large companies with a global mandate and initiative to implement a digital strategy, but when users actually need support on the gory details of data engineering and data pipelines in specific business units, support is lacking.  Instead, the initiative is rolled out at the corporate level but it is not well supported in the business unit where the focus should be on a specific problem in a specific basin. 

That brings me back to what I see here in Calgary. There are a lot of highly skilled technical people, but they appear to be quite siloed working on niche projects. It could be a function of the wrong expectations being set from the beginning. As per my previous comments, a successful digital strategy is really rooted in a management of change process that can be daunting and can make or break this kind of initiatives.

Do you see Birchcliff being on the cutting edge? And is that where you want to live or do you want to be somewhere in the early majority?

The leading edge is great, the bleeding edge is not so great. We’ve found that the analytics space is changing quickly; there’s a lot of smart suppliers and vendors developing and building tools and workflows. Looking at Birchcliff specifically, we try to balance this evolving space by always asking ourselves if we should build versus buy. Maybe you want to buy it because if you have to maintain and support it, the cost of maintenance can become prohibitive and is better suited for a 3rd party vendor to host it on the cloud. In addition, if the workflow or technology is evolving you can leverage of improvements that are driven by other operators as well where we may not use that specific application 24/7. 

So in general – I would say if it is truly novel we continue to develop it in house, simply because there is nothing like it on the market whereby we consider buying it if there are obvious advantages as previously highlighted.

Is there one specific project you could talk about that you were really proud of?

I guess there’s a lot of them. We’ve got workflows now for automated lookbacks on wells that are recently drilled. There’re field dashboards that we use to optimize production that have significantly impacted the bottom line. We’re using blending analytics tools to help our marketing group blend the right grades to maximize returns. There are ingestion tools that we’ve built for third-party pressure data that allows us to pipeline and database it. There are custom apps that we’ve built to house datasets that don’t typically have a home, where we have built processes that ingest that data and allow for easy access by our engineers. There are many examples.

Over the last few years, we’ve been spending more and more time building up competency at related to multivariate modeling and specifically machine learning projects. We are now a long way down that road where we have staged large ‘featurized’ data sets, allowing us to operationalize the type of workflows. We’re really excited about trying to blend a data-driven empirical data set with a physics-based model. That’s the stretch goal we’re working toward but expect to have a operationalized workflow by the end of this year.

Is Birchcliff using any cloud computing resources today?

We’re a bit of a hybrid. This is not my area of expertise. Some things are on the cloud. Some things are on-prem. Security, scalability, latency, control—all these things have pros and cons in both buckets, but we’re doing a little bit of both.

Control of the data: that was a really big thing. We own our plant. We don’t do contractors. We need to control everything, but now we’re starting to see the benefits of cloud, so we are trying to find a happy medium.

We haven’t seen any limitations with having on-prem servers during this time when everyone’s working remotely. Everyone’s on VPN and it was flawless for us. We didn’t skip a beat. 

Any books or blogs that you’d suggest reading?

I don’t seem to have too much time for reading between work, family and, of course, the mountains. I think Bill Gates always has a few interesting things to say in Gates Notes where he shares his perspective on the complicated challenges our world is facing today.

A second thought leader I enjoy learning from is Peter Tertzakian, a Geophysicist by training who is the Executive Director of the ARC Energy Research Institute. He’s basically a historian of energy transitions. He’s got a really cool website called Energyphile and has written several books as well. 

He also hosts the ARC Energy Ideas podcast with Jackie Forrest that explains the latest trends in Canadian energy and beyond. They’re not just focused on upstream E&P, they’re focused on energy.  

In your own words, how would you describe Petro.ai?

I think there’s a lot of shared vision with Petro.ai in terms of the value of analytics and where it could play a really critical role in this continuous improvement journey. I think you have a great group and a good culture. I’m really happy for the success that your team has seen. 

Theo van der Werken is a highly resourceful and results-oriented manager with a deep understanding of all aspects in upstream oil and gas disciplines. Specialties include asset management of tight oil and gas reservoirs with proven leadership experience.
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.

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.

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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.

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.
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Business Intelligence Tools Data Science & Analytics Drilling & Completions Geology & Geoscience Passion for Change Reservoir Engineering

Passion for Change: Bonanza Creek Energy

with Kyle Gorynski, Director Reservoir Characterization and Exploration at Bonanza Creek Energy

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

Why do you think there’s been so much hesitation around change?

There’s a strong engrained culture in oil and gas with  a generation of people who’ve been doing this for decades – although on completely different rocks, play types and extraction techniques. There’s been pushback on adopting new geology or engineering software because people become so comfortable and familiar with the tools they use. I think a lot of it is simply the unique culture in oil and gas. Shale is still new, and our guesses are still evolving. I think we’re getting closer and closer to what that right answer is.

New organizations will have to adopt new technologies and adapt to new trends, because there’s no other way to make this business work.

As a scientist or engineer or manager in this space, we really have to be cognizant that the goal of a lot of vendors out there is not to help you get the right answer, it’s to make money. The onus is on us to vet everyone and make sure we’re getting the answers we want. 

Machine learning is simply another tool, like a physics-based model, aimed to help us predict an outcome and increase the precision and accuracy of these predictions. People have made pretty fantastic inferences from these kinds of tools. 

You can’t just pay a company to apply machine learning to a project. You need to help them utilize the correct inputs, the relationships, and ensure the predictions and outcomes match the other observations you have from other datasets.

I don’t think any organization should be cutting-edge for the sake of being cutting-edge. The goal is to solve these very specific technical challenges quicker and with more accuracy. Our job is to extract hydrocarbons in the most safe, environmentally friendly, and economic fashion. Technology like machine learning and AI are tools that can help us achieve these goals and needs to be done correctly.

Can you share any successes around data science or machine learning at your company?

The industry has been using these techniques for a long time. In their simplest form, people have been cross-plotting data since the early days of the oil and gas industry, trying to build relationships between things. At the beginning of my career, I remember using neural networks to predict log response.

Now we use predictive algorithms to help us predict log response where we don’t have certain logs. Let’s say we want to predict lithologies—carbonate-clay-quartz-feldspar content in a well— we’ll build relationships between triple-combo logs, and more sophisticated, but scarce elemental capture spectroscopy logs. We don’t have ECS logs everywhere, but we have triple-combo everywhere, so if you can build a relationship between those, then you have a massive dataset you can use to map your asset. That’s a simple way we use this type of technology. 

Like almost every company now, we’re also predicting performance. That’s how we’re able to make live economic decisions. We have a tool where we can put in a bunch of geologic and engineering inputs and it’ll predict production rates through time that we can forecast, add new costs, and run economics live. We’re running Monte Carlo simulations on variable rates, volumes, lateral length, spacing, commodity pricing, and costs that are based in our best estimates to predict tens of thousands of outcomes to try to help us better understand what the best decision could possibly be. I think that’s the most impactful place it’s being used, and I think that trend is being adopted more and more in industry as I talk to my peers. 

Type curve generation is no longer grabbing a set of wells and grouping them together and fitting a curve to it, but it’s trying to predict the infinite amount of outcomes that are between the extremes.

Have you seen any success among your competitors using technology, specifically data science and analytics tools?

There’s some great work out there across the board. I had a lot of fun at Encana (now Ovintiv) seeing a lot of my peers who are exceptionally smart really trying to adopt new technology to solve problems. I’ve seen some amazing work getting people to adopt new ideas, new thoughts, new predictions. I like going to URTeC. I think that’s a fantastic conference. I always find a number of great sets of technical work that has come out. 

I think the industry is doing a great job. There’s a ton of really smart people out there that know how to do this work. I think a lot of young people are really adopting coding and this bigger picture approach to subsurface, where it’s not just you’re an engineer or you’re a geoscientist, you really have to understand the fluid, the pore system, the stresses, what you’re doing to it. There’s no way we can be impactful unless we understand the really big picture, and people are getting much better at that, trying to use tools and develop skillsets that allow them to solve these problems a lot quicker.

How would you describe Petro.ai?

We see you guys as filling a gap we have. It’s the ability to pull data together. It’s the ability to simply apply big data to a dataset we quite frankly don’t have the time or the capability to do in-house. Petro.ai provides us with a very important service that allows us to get to a point that would take us 12-18 months to get to on our own, but in only a couple months. What we really like about it is the fact that we’re developing something that’s unique and new and therefore has our input and involvement, so we’re not just sending you a dataset and asking for an answer, we’re trying to say what we think drives the results, and we also want your feedback. So you’re also a group of experts as well that not only have your own experiences, but you’ve seen people’s assets and plays and how everyone else in industry is looking at it, so it’s nice to have this group of consultants that have the same goal – to address a problem and try to figure it out. We want to get to an answer as quickly as we possibly can and start to apply those learnings as quickly as we possibly can. 

Kyle Gorynski is currently Director of Reservoir Characterization and Exploration at Bonanza Creek Energy.  Kyle previously worked at Ovintiv where he spent 7 years in various technical and leadership roles, most recently as the Manager of Reservoir Characterization for their Eagle Ford and Austin Chalk assets.  Although he is heavily involved on the technical side of subsurface engineering and geoscience, his primarily focus is on their practical applications in resource and business development . Kyle received his B.S. and M.S. in Geology from the University of Kansas in 2008 and 2011, respectively.
Categories
Drilling & Completions Passion for Change Reservoir Engineering

Passion for Change: Bonanza Creek Energy

with Kyle Gorynski, Director Reservoir Characterization and Exploration at Bonanza Creek Energy 

Tell us about your background and what you do now.

I’m from Kansas and got my Bachelor’s and Master’s degrees in Geoscience at University of Kansas, then moved straight out to Denver. Spent the first seven years of my career with Ovintiv, which was previously Encana, and had various roles, starting with mainly geology functions, and eventually working as a manager. I’ve always been interested in the technical side of the industry and novel approaches to petrophysics, geomechanics, and reservoir mapping, and how new data and new analyses can drive decision-making. It’s important that our decisions are driven through science and statistics and less through drillbit and opinion alone. I joined Bonanza Creek about a year and a half ago.

I’m the Director of Reservoir Characterization and Exploration. This role has two primary functions. One is an asset development function and the other is Business Development/Exploration. Asset development is the value optimization of our asset to maximize on key economic metrics by: 

  • understanding the subsurface to determine baseline performance
  • understanding key engineering drivers that impact performance 
  • applying those insights to modify things in real-time like spacing, stacking, completion design, well flowback etc.

At Bonanza Creek, one of the things our CEO Eric Greager likes to say is, “We’re unique because we have the agility of a small company, with the technical sophistication of a larger enterprise.” 

This allows us to respond to things that are changing quite rapidly, from the costs of goods and services to our own evolving understanding of the reservoir. By rapidly adapting, we’re able to maximize value and economic return.

That’s the main piece. The other part of the role is the exploration and business development function. We apply the same principles I just described to other assets inside and outside our basin and work with the greater operations and finance groups to determine an asset’s current value and what its potential future value could be.

How do you incorporate technology into your approach?

Technology is applied everywhere we possibly can. We have powerful technically savvy people who can develop tools and use tools to guide all our decision making. 

That’s where Petro.ai comes in. We need help building additional tools to make real-time decisions. That’s going to help us stay lean and agile but also make sure we have the right information to be making the most informed decisions. It comes down to the right data and the right people.

We need the ability to make decisions at multiple levels within an organization, so decisions can be made quickly without a top-down approach but with a high level of trust. We need to make sure the technical work is vetted and have a culture of best practices built-in for engineering and geoscience evaluation so we can have a lot of trust in our workflows. When that expertise is already built into tools—a lot of the equations, the input, the math—that helps us have trust in the inputs as well as the outputs and allows us to make those quick decisions.

How do you define real-time?

Our ultimate goal on the completions side is to be making real-time changes while we’re pumping – so minute by minute. That’s what motivated the project we have going with Petro.ai, to start turning knobs during the job, making sure the reservoir is sufficiently stimulated and not over capitalized. 

Let’s say we have sixteen wells per section permitted, but all of a sudden commodity prices drop and it’s at forty bucks, so we’ll only drill eight of those wells. Once those eight wells are in the ground, you’re stuck with that decision. Then maybe commodity prices go up, and we get a good price on sand or water, then we rerun the economics and what the type curves look like. Then we’ll make a new decision, for example, on the amount of sand or water or what the size of our stages are. 

We are making really quick decisions in terms of completions on a well-by-well basis. As price fluctuates and we’re teetering on the edge of break-even, we have to be real flexible in terms of trying to maximize the economics. 

Our next step with Petro.ai is using our 3D seismic data, well architecture, geosteering, and drilling data to understand what kind of rock we’re actually treating to make sure we’re putting a specific design for that specific formation, and we’re also reading the rock at the same time. We’re taking that information, which is mainly pressure response during the job, and trying to learn from that live.

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

I’m passionate about understanding the subsurface and the whole E&P industry and our evolving understanding of unconventionals. I’ve always been passionate about the big picture, trying to zoom out and understand how everything interconnects.

Change is a reality. It’s unavoidable. The pace of change and the path you take differs between organizations. The industry as a whole has been incredibly slow to adopt change. We’re now on the verge of a large extinction event. The E&P companies that remain will be in a better position to thrive once this is all over.

Unfortunately, something even as simple as generating a return on your investments has eluded many companies. For these companies, it is often the stubborn top-down culture that has resisted change and is their greatest detriment. It’s an exciting and scary time today. However, these events allow the best to survive and force others to adapt and change – for the better. 

Investments on a single well can be aprox. 6 to 10 million dollars for a 2-mile lateral in the U.S.. Investments on learning are 10s to 100s of thousands of dollars for an entire year. There’s a lot of capital destruction that could have been avoided by doing homework, collecting data, doing analysis, and connecting the dots from math to modeling to statistics all the way to a barrel of oil.

Science often ends up in a folder. It takes good leadership, management, and technical work to ensure that you’re making decisions with all your data and information. The point is to make better decisions and to make better wells. 

The conversation continues here.

Kyle Gorynski is currently Director of Reservoir Characterization and Exploration at Bonanza Creek Energy.  Kyle previously worked at Ovintiv where he spent 7 years in various technical and leadership roles, most recently as the Manager of Reservoir Characterization for their Eagle Ford and Austin Chalk assets.  Although he is heavily involved on the technical side of subsurface engineering and geoscience, his primarily focus is on their practical applications in resource and business development . Kyle received his B.S. and M.S. in Geology from the University of Kansas in 2008 and 2011, respectively.