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.