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 Industry. I 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.