AI-Driven Approach for Sustainable Extraction of Earth's Subsurface Renewable Energy While Minimizing Seismic Activity

Abstract

Deep geothermal energy, carbon capture and storage, and hydrogen storage hold considerable promise for meeting the energy sector's large-scale requirements and reducing ${\text{CO}}_2$ emissions. However, the injection of fluids into the Earth's crust, essential for these activities, can induce or trigger earthquakes. In this paper, we highlight a new approach based on reinforcement learning (RL) for the control of human-induced seismicity in the highly complex environment of an underground reservoir. This complex system poses significant challenges in the control design due to parameter uncertainties and unmodeled dynamics. We show that the RL algorithm can interact efficiently with a robust controller, by choosing the controller parameters in real time, reducing human-induced seismicity, and allowing the consideration of further production objectives, for example, minimal control power. Simulations are presented for a simplified underground reservoir under various energy demand scenarios, demonstrating the reliability and effectiveness of the proposed control–RL approach.