The barrel balance: how generative AI transforms exploration economics and introduces a Green Tax on computing

This paper examines the dual impact of artificial intelligence on the oil and gas industry. On the one hand, the implementation of neural network methods for seismic data interpretation, reservoir digital twins, and predictive diagnostic systems can significantly reduce operating and capital costs. Real-world cases demonstrate an increase in exploration drilling success rates to 67%, a reduction in unplanned downtime by 18-30%, and an additional production gain of up to 2,3% at mature fields. On the other hand, training and operating large generative models and geoscience foundation models require enormous energy consumption – up to tens of gigawatt-hours per single model, which is comparable to the annual electricity consumption of hundreds of households. In oil-producing regions such as Khanty-Mansiysk Autonomous Okrug, AI infrastructure competes with core production for power capacity, while using associated petroleum gas to power computing units does not solve but rather exacerbates the environmental problem.