Climate change is a pressing global issue, with the global average temperature rising to unprecedented levels, threatening the planet's habitability. Subsurface applications, such as subsurface remediation, carbon capture and storage (CCS), geothermal energy, and subsurface storage of renewable energy, hold great potential in addressing climate change and reducing greenhouse emissions. However, decision-making in subsurface operations is challenging due to the uncertainties inherent in subsurface environments and the long-term consequences of actions. As a result, we need a robust decision-making method capable of handling a wide range of scenarios in the subsurface. 

This dissertation examines widely adopted decision-making approaches in subsurface applications, identifying their limitations and proposing the use of Partially Observable Markov Decision Processes (POMDPs) as a more effective alternative. The thesis aims to bridge the gap between the subsurface and POMDP communities by providing comprehensive guidance on formulating and solving POMDPs for subsurface applications. Two examples, groundwater contaminant remediation and CCS, are presented to demonstrate the superiority of POMDPs performance compared to baseline approaches. This work also advocates for the increased adoption of POMDPs in subsurface applications, with the potential to revolutionize decision-making processes and contribute significantly to mitigating the effects of climate change.