Efficient data‐worth analysis for the selection of surveillance operation in a geologic CO₂ sequestration system

Abstract

In this study, we propose an approach to selecting an appropriate surveillance operation in a geologic CO2 sequestration, 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. The proposed approach is demonstrated numerically for selecting a surveillance operation and assessing the reduction of uncertainties in predicting CO2 leakage from abandoned wells during geologic CO2 sequestration. 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.© 2015 Society of Chemical Industry and John Wiley & Sons, Ltd

Managing Uncertainty in Geological CO2 Storage Using Bayesian Evidential Learning

“…Several studies have demonstrated the application of a data assimilation and optimization strategy for the minimization and mitigation of risks during CO 2 injections as well as the postinjection period at the storage sites. For instance, Dai et al [10] introduced a method of analyzing data by employing the probabilistic collocation-based Kalman filter (PCKF) for the optimization of the surveillance operations within GCS projects. The method involves the development of surrogate models with the use of polynomial chaos expansions (PCE) that act as a replacement of the original flow model, followed by an assessment of the reduced variance of the field cumulative CO 2 leak by analyzing the data.…”

Monitoring Brine Leakage From Deep Geologic Formations Storing Carbon Dioxide: Design Framework Validation Using Intermediate‐Scale Experiment

“…Installing a large number of sensors in deep overlying formations of a CO 2 storage zone (1–2 km deep) is expected to be cost‐prohibitive (Hovorka et al., 2013; Nordbotten et al., 2004; Tsang et al., 2008; Vermeul et al., 2016) and might also pose safety risks (Kelessidis, 2009; Liu, et al., 2013). Various methods have been developed to optimize the number of needed observation points to reduce the monitoring cost (Chen et al., 2017; 2018; Dai et al., 2015, 2016; Jeong et al., 2019; Seto & McRae, 2011a, 2011b; Yang et al., 2011, 2017; Yonkofski et al., 2016).…”