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A POMDP Model for Safe Geological Carbon Sequestration
Anthony Corso · Yizheng Wang · Markus Zechner · Jef Caers · Mykel J Kochenderfer

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in Tackling Climate Change with Machine Learning »
Event URL: https://www.climatechange.ai/papers/neurips2022/25 »

Geological carbon capture and sequestration (CCS), where CO2 is stored in subsurface formations, is a promising and scalable approach for reducing global emissions.However, if done incorrectly, it may lead to earthquakes and leakage of CO2 back to the surface, harming both humans and the environment. These risks are exacerbated by the large amount of uncertainty in the structure of the storage formation. For these reasons, we propose that CCS operations be modeled as a partially observable Markov decision process (POMDP) and decisions be informed using automated planning algorithms. To this end, we develop a simplified model of CCS operations based on a 2D spillpoint analysis that retains many of the challenges and safety considerations of the real-world problem. We show how off-the-shelf POMDP solvers outperform expert baselines for safe CCS planning. This POMDP model can be used as a test bed to drive the development of novel decision-making algorithms for CCS operations.

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Author Information

Anthony Corso (Stanford University)
Yizheng Wang (Stanford Univerity)
Markus Zechner (Stanford University)
Jef Caers (Stanford University)
Mykel J Kochenderfer (Stanford University)

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