mlsanityData to 14 Oct 2026

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

arXiv 2408.03664 ↗First posted 7 Aug 2024cs.LG cs.SY eess.SYcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

Deep Geothermal Energy, Carbon Capture and Storage, and Hydrogen Storage hold considerable promise for meeting the energy sector's large-scale requirements and reducing CO2 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, textite.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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 6 of its 48 references that are in the map. First posted on arXiv 7 Aug 2024.

Builds on

6 of 48 references.

References · 6

  1. 2015Continuous control with deep reinforcement learningcited by 3,407
  2. 2021Controlling earthquake-like instabilities using artificial intelligencecited by 1
  3. 2022Passivity-based control of underactuated mechanical systems with Coulomb friction: Application to earthquake preventioncited by 1
  4. 2022MIMO Super-Twisting Controller using a passivity-based designcited by 1
  5. 2023Insights of using Control Theory for minimizing Induced Seismicity in Underground Reservoirscited by 1
  6. 2024Adaptive Gain Scheduling using Reinforcement Learning for Quadcopter Controlcited by 1

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  1. 2026Efficient Geothermal Well-Control Optimization via Diffusion-Surrogate Reinforcement Learning
  2. 2024Optimization of geological carbon storage operations with multimodal latent dynamic model and deep reinforcement learning
  3. 2026Closed-Loop CO2 Storage Control With History-Based Reinforcement Learning and Latent Model-Based Adaptation
  4. 2024Reinforcement Learning for Sustainable Energy: A Survey
  5. 2026The Challenges of Using Reinforcement Learning for Controlling Industrial Energy Systems
  6. 2021DeepThermal: Combustion Optimization for Thermal Power Generating Units Using Offline Reinforcement Learning
  7. 2026Safe Deep Reinforcement Learning for Building Heating Control and Demand-side Flexibility
  8. 2024Reinforcement Learning for Efficient Design and Control Co-optimisation of Energy Systems