By Mark Jaffe, EUCI energy writer
The use of artificial intelligence could unlock $230 billion in savings for oil and gas operators and oilfield service companies, according to a McKinsey & Co. white paper.
There are a variety of oilfield operations that lend themselves to AI management and optimization for they generate measurements and data on performance.
“The challenge is where to concentrate, how to scale, and how to share value when efficiency reduces the activity that many contracts reward,” the report said.
There are big savings to be gained in oilfield development, production and reservoir management.
“AI can unlock approximately $65 billion in annual recurring value across upstream oil and gas in the near term with today’s technology, and there is a credible path to $230 billion at full potential,” according to the analysis.
McKinsey estimated AI-driven improvements in exploration success could add more than $35 billion annually in balance sheet value through accretion.
The estimates are net of the cost of development of AI with its computers, software, data infrastructure, and talent, which adds up, by McKinsey estimates, to more than $30 billion.
The biggest value will be in a few specific areas, so “the companies that are likely to succeed in capturing value from AI will be those that focus relentlessly on the handful of use cases where potential value is greatest,” McKinsey said.
There are a few keys areas with large pools of economic value that generate high-frequency operational data and are governed by physical systems where feedback can be observed.
Autonomous artificial lift and production optimization is a prime candidate. AI has the ability to optimize rod pumps, electric submersible pumps, gas lifts, waterfloods, production networks, surface facilities, flow assurance, and chemical programs.
Drilling and well delivery – one of upstream’s largest outsourced expenditures to service companies – is another area where AI can make an impact.
Well development involves directional drilling, fluids for hydrofracturing, management of flowback fluids, cementing, bits, and logging.
“AI can improve well planning and equipment selection, reduce tripping and connection time, provide real-time and early anomaly detection,” the analysis said. It can also aid in optimizing well placement.
All this can lead to more efficient operational sequencing and cut downtime on a well pad.
Intelligent reservoir management is another area ripe for AI deployment, which can “accelerate subsurface interpretation, improve static and dynamic model updates, generate surrogate models for faster simulation, support recovery strategy, improve reserves estimation, and help teams make better decisions,” the report said.
AI is “a double-edge sword” for the oilfield services sector, McKinsey said. On the one hand, $17 billion in oilfield service revenue could be lost to AI-driven efficiencies. The revenue exposure could reach $60 billion as AI use expands.
On the other hand, service companies will also shed the variable costs associated with crew, equipment, consumables, and logistics. The McKinsey analysis estimates that at typical industry margins of about 40%, the real cash flow impact is about $7 billion at near-term potential and $24 billion at full potential.
“Capturing the next wave of AI value in upstream requires a change in approach,” McKinsey said. “The industry may not need more pilots or proofs of concept. It likely needs fewer and bigger bets, clearer ownership of solutions and data, industrialized deployment, and commercial models that reward outcomes.”
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