  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.
