Deep geothermal energy, carbon capture and storage, and hydrogen storage hold considerable promise for meeting the energy sector's large‐scale requirements and reducing 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.

中文翻译：

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AI 驱动的方法，可持续地开采地球地下可再生能源，同时最大限度地减少地震活动


深层地热能、碳捕获和储存以及氢储存在满足能源行业的大规模需求和减少排放方面具有相当大的前景。然而，向地壳注入这些活动所必需的液体可能会诱发或引发地震。在本文中，我们重点介绍了一种基于强化学习 （RL） 的新方法，用于在地下储层的高度复杂环境中控制人为诱发的地震活动。由于参数不确定性和未建模的动力学，这个复杂的系统给控制设计带来了重大挑战。我们表明，RL 算法可以通过实时选择控制器参数、减少人为引起的地震并允许考虑进一步的生产目标（例如，最小控制功率）来与稳健的控制器进行高效交互。对各种能源需求情景下的简化地下储层进行了仿真，证明了所提出的控制-RL 方法的可靠性和有效性。