AI-Driven approach for sustainable extraction of earth's subsurface renewable energy while minimizing seismic activity

TL;DR

This paper presents a reinforcement learning-based control method to minimize human-induced seismicity during underground energy extraction, effectively balancing seismic risk and energy production objectives.

Contribution

It introduces a novel RL approach integrated with robust control for real-time seismicity management in complex underground reservoirs.

Findings

RL reduces seismic activity effectively across scenarios

The method balances seismic risk with energy production goals

Simulation results demonstrate reliability and adaptability

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 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 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 reinforcement learning 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, \textit{e.g.}, 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-reinforcement learning approach.