A POMDP Model for Safe Geological Carbon Sequestration

TL;DR

This paper models geological carbon sequestration as a POMDP to improve safety decision-making under uncertainty, demonstrating the effectiveness of automated planning algorithms over expert baselines.

Contribution

It introduces a simplified POMDP model for CCS operations that captures key safety challenges and applies existing solvers to enhance decision-making.

Findings

POMDP-based planning outperforms expert decisions in safety tasks.

The model effectively captures uncertainty in subsurface formations.

Automated algorithms can improve safety in CCS operations.

Abstract

Geological carbon capture and sequestration (CCS), where CO$_2$ 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$_2$ 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.