Initially slow to embrace the artificial intelligence (AI) revolution, the US shale sector is fast playing catch-up, looking to harness AI's power to cut costs and speed up drilling.
While attention up until now has centred on the vast energy needs of data centres supporting AI, there is a growing buzz around the technology's potential to help producers keep the oil flowing by allowing for the monitoring of well sites remotely, and to save on maintenance by figuring out problems in advance. AI could also play a key role in detecting and curbing emissions, help companies do a better job of mapping out discoveries, and improve well and completion designs. Given forecasts for shale to peak in the coming years, more advanced drilling techniques backed by AI could help delay the inevitable.
"There's definitely a lot of potential for generative AI to extend the shale era if you look at it both from the standpoint of what it brings to the table, as well as the incremental energy requirement of powering the AI revolution," consultancy Enverus' director of product innovation, Akash Sharma, says.
So far, a reluctance to share data for fear of losing a competitive edge, and concerns over data security, have been cited as key factors holding it back. There has also been a lack of understanding regarding the potential of AI to transform an industry that has long been at the forefront in technology breakthroughs. That is now changing, with AI and machine learning mentioned more often on earnings calls as executives seek to build on recent efficiency gains. Given shale's low recovery rates — compared with conventional reserves — AI could prove a boon.
Only this week, SLB — formerly known as Schlumberger — the world's biggest oil field contractor, deepened ties with US computing giant Nvidia by agreeing to work on generative AI projects. "As we navigate the delicate balance between energy production and decarbonisation, generative AI is emerging as a crucial catalyst for change," SLB chief executive Olivier Le Peuch said.
Around 30pc of the costs of drilling a new shale well could be reduced by AI, according to Goldman Sachs. A hypothetical AI-induced 10-20pc jump in the recovery factors of shale could increase reserves by 8-20pc, or the equivalent of 10bn-30bn bl, according to the bank. Increased use of AI offers the potential to "increase the ultimately recoverable resource base, delaying further the peak of US shale supply", Goldman analysts wrote in a recent note.
Tipping point
Around half of executives from large exploration and production (E&P) firms — those with output of at least 10,000 b/d — reported using some form of AI in an energy survey carried out by the Dallas Fed in June. The share was lower among services firms and lower still among small E&P operators. Asked about the main benefits of AI, the most common response was increased productivity, followed by access to better or more timely information, and then lower costs.
Smaller private operators that have an eye on the exit might be unwilling to invest in AI, given the cost involved, a strategy that could prove short-sighted. "They view it as, ‘I'm only going to be in business for a few years and it's a lot of effort'," Quantum Capital Group founder Wil VanLoh said earlier this year.
But as scale and consolidation become key shale drivers, the industry might also be closer to a tipping point in terms of the number of companies adopting AI or considering using it. "I am having significantly more conversations today than, let's say, in March of this year, with people in the energy space around AI," Enverus' Sharma says. Energy companies that embrace AI will maintain a significant competitive advantage over those that do not, according to VanLoh.
