Selecting Appropriate Surveillance Operation for Geologic CO2 Sequestration by Efficient Data-Worth Analysis

Geologic CO2 sequestration (CCS) is considered as one of the feasible options to reduce CO2 emissions in recent years. Due to the uncertainties in modeling the CCS process, surveillance operations are needed to collect data for history-matching. However, different operation scheme may have different type of data, number of observation wells, and observation frequency, which can lead to different history-matching results. Hence, it is necessary to determine the optimal surveillance operation. Traditional methods try to combine data-worth analysis with history-matching method to solve the problem. However, it is computational demanding when the Monte Carlo based history matching methods are used which require a large number of realizations to maintain converge.In this study, we propose an approach to selecting an appropriate surveillance operation in a CCS system, through efficient data-worth analysis with the probabilistic collocation based Kalman Filter (PCKF). A surrogate model with polynomial chaos expansion is constructed by performing a small number of flow simulations, based on which history matching is implemented with the observations from the surveillance operations. In our study, we first demonstrate the PCKF can be used to capture the formation properties precisely by comparing with the traditional history-matching method. Then, the integrated approach combining data-worth analysis and the PCKF is applied to compare seven different surveillance operation schemes in a CCS system. The proposed approach is demonstrated numerically for selecting a surveillance operation and assessing the reduction of uncertainties in predicting CO2 leakage from abandoned wells. Our results reveal that the proposed approach of data-worth analysis can be utilized to select an appropriate surveillance operation in a geologic CO2 system, with a small computational effort.