A POMDP Model for Safe Geological Carbon Sequestration
Abstract
Geological carbon capture and sequestration (CCS), where CO 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 CO 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.
Cite
@article{arxiv.2212.00669,
title = {A POMDP Model for Safe Geological Carbon Sequestration},
author = {Anthony Corso and Yizheng Wang and Markus Zechner and Jef Caers and Mykel J. Kochenderfer},
journal= {arXiv preprint arXiv:2212.00669},
year = {2022}
}
Comments
Accepted at NeurIPS 2022 Workshop on Tackling Climate Change with Machine Learning