Abstract
Carbon capture and sequestration (carbon capture and storage or CCS) represents a unique potential strategy that can minimize CO2 emissions in the atmosphere, and it creates a pathway toward a neutral carbon balance, which cannot be solely achieved by combining energy efficiency and other forms of low carbon energy. To contribute to the decision-making process and ensure that CCS is successful and safe, an adequate monitoring program must be implemented to prevent storage reservoir leakage and contamination of drinking water in groundwater aquifers. In this paper, we propose an approach to perform value of information (VOI) analyses to address sequential decision problems in reservoir management in the context of monitoring the geological storage of CO2 operations. These sequential decision problems are often solved and modeled by approximate dynamic programming (ADP), which is a powerful technique for handling complex large-scale problems and finding a near-optimal solution for intractable sequential decision-making. In this study, we tested machine learning techniques that fall within ADP to estimate the VOI and determine the optimal time to stop CO2 injections into the reservoir based on information from seismic surveys. This ADP approach accounts for both the effect of the information obtained before a decision and the effect of the information that might be obtained to support future decisions while significantly improving the timing, value of the decision, and uncertainty of the CO2 plume behavior, thereby significantly increasing economic performance. The Utsira saline aquifer west of Norway was used to exemplify ADP’s ability to improve decision support regarding CO2 storage projects.
Highlights
Carbon capture and storage (CCS) is increasingly considered a promising strategy for reducing CO2 emissions
Terminal analysis involves the evaluation of selection between alternatives after a test has been conducted and the data have been gathered, whereas value of information (VOI) analysis, often called ‘‘preposterior analysis’’ (Raiffa and Schlaifer, 1961), regards the decision problem as it appears before a test has been conducted
We presented a VOI framework that can be used to compute the VOI in a reservoir development plan
Summary
Carbon capture and storage (CCS) is increasingly considered a promising strategy for reducing CO2 emissions. Anyosa et al (2021) developed a statistical learning method to assess the probability of an early CO2 leakage detection through a key fault at the Smeaheia site and conducted a VOI analysis of monitoring strategies, considering an underlying decision situation connected to continued injection of CO2, or termination of this process. In this setting, geophysical monitoring is valuable if it leads to improved decisions for the injection program. We offer concluding remarks and recommendations for future research directions
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