Optimizing Carbon Storage Operations for Long-Term Safety
This work addresses the challenge of safely sequestering CO2 in geological formations for carbon capture and storage, which is crucial for mitigating climate change risks, but it appears incremental as it builds on existing POMDP methods with adaptations like surrogate models.
The study tackled the problem of optimizing carbon storage operations for long-term safety by modeling it as a partially observable Markov decision process (POMDP) and using belief state planning, resulting in effective safe storage in simulations with flexibility demonstrated through different monitoring strategies and a neural network surrogate model.
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.