Summary of Ai-driven Approach For Sustainable Extraction Of Earth’s Subsurface Renewable Energy While Minimizing Seismic Activity, by Diego Gutierrez-oribio et al.
AI-Driven approach for sustainable extraction of earth’s subsurface renewable energy while minimizing seismic activity
by Diego Gutierrez-Oribio, Alexandros Stathas, Ioannis Stefanou
First submitted to arxiv on: 7 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel reinforcement learning-based approach is proposed to mitigate human-induced seismicity in underground reservoirs, which is crucial for large-scale energy applications such as deep geothermal energy, carbon capture and storage, and hydrogen storage. The complex environment of the underground reservoir poses significant challenges due to parameter uncertainties and unmodeled dynamics. The proposed method uses a robust controller that interacts efficiently with the reinforcement learning algorithm, which chooses controller parameters in real-time to minimize seismicity and meet production objectives. Simulations are presented for various energy demand scenarios, demonstrating the reliability and effectiveness of the approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to control underground reservoirs to reduce earthquakes caused by human activities. Underground reservoirs are important for storing carbon dioxide, producing hydrogen, and generating geothermal energy. But injecting fluids into these reservoirs can cause earthquakes. The authors developed an algorithm that learns how to control the injection of fluids to minimize the risk of earthquakes. They tested this algorithm in simulations and found it effective in reducing seismicity while still meeting production goals. |
Keywords
» Artificial intelligence » Reinforcement learning
