On November 25th, Petro.ai Founder and CEO, Dr. Troy Ruths, was a guest on the Invest with James West podcast series hosted by James West, Senior Managing Director & Partner at Evercore ISI. During the 30-minute podcast, James and Troy discuss trends of artificial intelligence and machine learning in the oil and gas industry and how Petro.ai is changing the way E&P companies plan, develop, and operate their assets.
The Role or AI
The creation and application of artificial intelligence requires a lot of data. Oil and gas operators have always generated large quantities of data, but the massive increase in activity the industry has seen as a result of unconventionals created an ideal environment for AI. Each well, and even each stage, can be seen as a unique data point where operators are constantly changing and experimenting. The real power of AI is in unlocking all this data.
“People think of AI at the top of the pyramid,” says Troy. “But the future is with AI at the bottom of the pyramid—the new backbone that serves information up to the enterprise, and humans are going to remain at the top of the pyramid.” This view represents a departure from how many individuals see AI but promises a much greater impact to operators. Engineers today think about their data in terms of spreadsheets or databases. The data layer of the future provides significantly more context while being much more intuitive. This is the role played by Petro.ai, intelligently storing, integrating, and activating more than 60 types of oil and gas specific data, as well as associated metadata. Many of these data types that are ingested by Petro.ai, like microseismic events, fiber, or electromagnetic imaging data don’t have a standard home today.
Challenges to AI Adoption and Change
“I would negatively correlate ability to adopt new technology to oil price. The better the oil price is, the harder it is to get technology adoption,” remarked Troy. The current price environment is ideal for technology adoption, especially when it comes to AI. Operators are at a point now where they need digital tools to help them do more with less. The other impediment to AI adoption revolves around education. AI can mean a lot of different things to different people and there is a level of education that still needs to take place to inform the industry on how AI can best fit into their organizations.
Troy goes on to explain another challenge, “AI can only extrapolate from what it’s seen, and that can be a problem in a world where the solution may be outside of what we’ve actually tried in the past.” Petro.ai incorporates principles of geomechanics into our workflows, bridging the gap between what we know from physics with machine learning.
AI in Upstream O&G
When prompted by James on the differentiated approach upstream analytics, Troy noted that “A lot of the new software that has entered the space is focused on operational efficiency and labor.…but honesty, those aren’t going to be needle moving enough for the industry. We’re focused on the needle moving problem, which is how can we reengineer. We need to reengineer how we approach these unconventional assets.” Good engineering done in the office is going to drive real improvements.
With recovery factors, well spacing, or frac hits, operators really need to focus on the productivity drivers for a resource unit. These questions cannot be investigated in isolation and some of the best practices we have seen come from bundling disparate workflows together. For example, a completions engineer may want to look at several different data types simultaneously. They may want to look at and ask questions about geology, drilling or surface constraints. This example goes back to humans being on top of the pyramid. The engineer needs to be fed with the relevant information, which is where AI can really help. Petro.ai not only serves up this data, but also uses a complex system model built using geomechanics and machine learning that takes engineers through an 8-step workflow to understand the key productivity drivers for a resource unit.
The industry has clearly learned that unconventionals are extremely difficult to develop profitably – even in the Permian. These are very complex systems with stacked pay that will require good engineering to be properly developed. This is good news for digital companies in 2020. In a broader sense, Troy sees operators evolving “towards surgical development, we’re going to go away from factory drilling and go more towards surgical.” However, some operators are clearing embracing digital more than others and so we expect a clear bifurcation in operator performance.
Listen to the podcast for the full discussion on AI and machine learning in oil and gas and the future for data in the energy sector.
Charles is the VP of Business Development at Petro.ai. Prior to joining Petro.ai, Charles spent ten years with Schlumberger in roles ranging from new product development in Houston to technical sales in Malaysia and operations management in Angola. He holds a BS. and MS. in mechanical engineering from the University of Illinois at Urbana-Champaign and a MBA from Rice University.