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

arXiv: https://arxiv.org/abs/2408.03664 · HTML: https://mlsanity.com/paper/2408.03664/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 6 of 6)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Controlling earthquake-like instabilities using artificial intelligence](https://mlsanity.com/paper/2104.13180/) (2021)
- [Passivity-based control of underactuated mechanical systems with Coulomb friction: Application to earthquake prevention](https://mlsanity.com/paper/2207.07181/) (2022)
- [MIMO Super-Twisting Controller using a passivity-based design](https://mlsanity.com/paper/2208.04276/) (2022)
- [Insights of using Control Theory for minimizing Induced Seismicity in Underground Reservoirs](https://mlsanity.com/paper/2310.02700/) (2023)
- [Adaptive Gain Scheduling using Reinforcement Learning for Quadcopter Control](https://mlsanity.com/paper/2403.07216/) (2024)

## 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.