  To combat global warming and mitigate the risks associated with climate
change, carbon capture and storage (CCS) has emerged as a crucial technology.
However, safely sequestering CO2 in geological formations for long-term storage
presents several challenges. In this study, we address these issues by modeling
the decision-making process for carbon storage operations as a partially
observable Markov decision process (POMDP). We solve the POMDP using belief
state planning to optimize injector and monitoring well locations, with the
goal of maximizing stored CO2 while maintaining safety. Empirical results in
simulation demonstrate that our approach is effective in ensuring safe
long-term carbon storage operations. We showcase the flexibility of our
approach by introducing three different monitoring strategies and examining
their impact on decision quality. Additionally, we introduce a neural network
surrogate model for the POMDP decision-making process to handle the complex
dynamics of the multi-phase flow. We also investigate the effects of different
fidelity levels of the surrogate model on decision qualities.
