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Efficient data‐worth analysis for the selection of surveillance operation in a geologic CO 2 sequestration system
Author(s) -
Dai Cheng,
Li Heng,
Zhang Dongxiao,
Xue Liang
Publication year - 2015
Publication title -
greenhouse gases: science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.45
H-Index - 32
ISSN - 2152-3878
DOI - 10.1002/ghg.1492
Subject(s) - probabilistic logic , computer science , kalman filter , data mining , selection (genetic algorithm) , carbon sequestration , geological survey , operations research , geology , artificial intelligence , engineering , ecology , carbon dioxide , biology , paleontology
In this study, we propose an approach to selecting an appropriate surveillance operation in a geologic CO 2 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 CO 2 leakage from abandoned wells during geologic CO 2 sequestration. Our results reveal that the proposed approach of data‐worth analysis can be utilized to select an appropriate surveillance operation in a geologic CO 2 system, with a small computational effort.© 2015 Society of Chemical Industry and John Wiley & Sons, Ltd