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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. 
 
Categories
Database, Cloud, & IT Updates

Petro.ai and AWS partner to operationalize modern analytics and reduce legacy infrastructure costs in oil and gas

Petro.ai, experts in machine learning and AI for geotechnical data science and providers of the industry-leading integrated analytics platform, announces a new offering enabling modern analytics from legacy data lakes using integrated data ingestion pipelines on Amazon Web Services (AWS). Petro.ai on AWS leverages the scalability and flexibility of AWS infrastructure to accelerate discovery of oil and gas insights through the fusion of operational and subsurface data. 

“While our industry has done a great job collecting and storing data in internal systems, building new value with this data has been incredibly difficult.”

Dr. Troy Ruths, Founder and CEO of Petro.ai

“The next wave of optimization requires two things: lower costs and enhanced outcomes. Petro.ai on AWS delivers immediate savings versus legacy systems while enabling more agility and better insights through the fusion of subsurface and operational data,” explained Dr. Troy Ruths, Founder and CEO of Petro.ai.

The automated integration of Petro.ai with Amazon S3, AWS Lambda, and AWS VPC minimizes time spent loading, normalizing, and managing data while providing the elastic compute power required for modern analytics at enterprise scale. 

Petro.ai’s proprietary technology incorporates AI and machine learning to facilitate the amalgamation of over 50 oil and gas data types, delivering integrated analytics and cross-disciplinary collaboration. 

Petro.ai supports collaborative analytics that span the entire upstream value chain—drilling, completions, and production. Quickly operationalize digital strategies with pre-built modules. Mitigate risk by deploying software workflows being used internationally across multiple basins and use cases. Supercharge internal teams with an open system that allows for easy data access and custom development.

Petro.ai is quickly becoming an industry standard with marquee customers across the globe including Diamondback Energy (Nasdaq: FANG) and Noble Energy (Nasdaq: NBL).

Categories
Geology & Geoscience

Geomechanics Comes into Focus

Highlights from the Invest with James West podcast with Dr. Troy Ruths, Founder and CEO of Petro.ai, and Dr. Mark Zoback, Stanford University Professor of Geophysics, Director of the Stanford Natural Gas Initiative, and Technical Advisor at Petro.ai

Listen now.

James West: What is geomechanics and why does it apply to the development of unconventional reservoirs?

Mark Zoback: To me, it’s the integration of the physical properties of the geologic formation, the fractures, and the other attributes that they have in a geomechanical sense. But, most importantly, it’s the forces that are acting in the rocks. That’s at the core of development of unconventional resources, because the key technology is horizontal drilling and multistage hydraulic fracturing.

Hydraulic fractures follow the stresses in the rock. In other words, once you know what the forces are, you can be predictive about what the hydraulic fractures would like to do.

JW: At this stage, how is machine learning and AI being applied?

Troy Ruths: In a previous podcast, I talked about AI being at the bottom of the pyramid. In this respect, what we’re trying to do is make it easier for the end user to get access to data types, run the interpretation, and then collaborate and predict on those within the context of geomechanics. 

The other big area is fingerprinting patterns. Let’s say you have a productivity pattern or that one interval is more productive than the other. If we can tie that to a key variable going through a nice technical analysis from the perspective of geomechanics, then you can go look for that fingerprint elsewhere.

We’ve had a lot of success doing that and I think that’s two big places where AI and Machine Learning have really helped act as a catalyst for a lot of these principles that Mark has put together in the book that he just put out.

MZ: Even after a couple of hundred thousand wells, which means a couple of millions of hydraulic fractures, we still have recovery factors of only 25% for gas and less than 10% for oil. So, we’re leaving more than 90% of the oil behind.

We can take these new ideas and then using the tools of Petro.ai, test those ideas against existing data and frame the problem in a whole new way to gain understanding. It’s one thing to have an idea, but it’s another thing to know whether that idea is going to work before you try to implement that idea at scale.

That combination of bringing new ideas and then confirming the applicability of those new ideas in an area of particular interest to a particular company—that’s where we’re going to really leverage these new ideas. We can figure out what’s important and what’s not and then try to attack this recovery factor problem, because we haven’t solved that through brute force and trial and error.

All of these questions surround the idea of vertical hydraulic fracture growth. We think about hydraulic fractures growing horizontally away from the horizontal well, but they also grow vertically. That’s controlled by the forces in rock and how you do the hydraulic fracturing. Well, if hydraulic fractures are growing up, they’re not growing out. The issues of well spacing, infill drilling, and stacked pay are all linked in a three-dimensional way to a condition presented to you by the Earth. You can’t change that. But, if you can characterize it and link it to the completion process, you have a shot at optimizing.

JW:   If we defined the 2017 to 2019 era in US onshore as what my colleague Steve Richardson eloquently pointed out as “the megapad misstep era.” What would you say were the reasons some of the companies erred and how they corrected course since then?

TR:   There’s substantial interaction between parent wells and wells that are landed in different zones. So, the assumption that you can take a type curve and multiply it by well counts has been proven not to be a viable way to understand how you’re exploiting that cube. 

When you step into that 3D problem, you need to take into account the vertical propagation of fractures and the interactions of wells that are brought online at the same time versus brought online at different times. 

All of that is explainable through these concepts that Mark is talking about and is something that you can measure and actually infer ahead of time. 

As we step into this next era of megapads, people are realizing that they just can’t develop intervals. They need to develop the entire pad together. 

JW:   So what inning do you think the North American unconventional oil and gas industry is in now in terms of drilling and completion efficiencies?

MZ:   I’d say we’re in the sixth or seventh inning with respect to drilling efficiencies. It’s remarkable what’s being done out there in terms of drilling and completions efficiency, but I think we’re in the second inning when it comes to understanding about what should be done. We know how to do it. We know how to do it efficiently, but I don’t think we really understand. We have a lot of data under our belt now, but there are literally millions of wells that could be used to exploit unconventional hydrocarbons in the Lower 48. Before taking advantage of any of these opportunities, we have to start incorporating a better understanding of what to do regardless of how efficiently it could be done.

JW:   How do you and the geomechanics team bring it all together at Petro.ai?

TR:   You know James, we’re trying to reinvent the workflow and I think we’ve talked about this in a lot of different ways. When I put the Petro.ai team together with Mark, I wanted to provide a team and vision that our clients could get behind and really help us reinvent the workflow. It’s going to be working with our clients to understand their challenges and their assets, but also bringing a lot of these new concepts to the table.

I really think that we’re one of the few players in the space that can bring this level of insightfulness and technical expertise, while at the same time, leveraging those millions of data points that a company is sitting on.