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Efficient data‐worth analysis for the selection of surveillance operation in a geologic CO 2 sequestration system

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  • Cheng Dai
  • Heng Li
  • Dongxiao Zhang
  • Liang Xue

Abstract

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

Suggested Citation

  • Cheng Dai & Heng Li & Dongxiao Zhang & Liang Xue, 2015. "Efficient data‐worth analysis for the selection of surveillance operation in a geologic CO 2 sequestration system," Greenhouse Gases: Science and Technology, Blackwell Publishing, vol. 5(5), pages 513-529, October.
    • Handle: RePEc:wly:greenh:v:5:y:2015:i:5:p:513-529
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    File URL: http://hdl.handle.net/10.1002/ghg.1492
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    1. Sudret, Bruno, 2008. "Global sensitivity analysis using polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 93(7), pages 964-979.
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    Cited by:

    1. Chen, Bailian & Harp, Dylan R. & Lin, Youzuo & Keating, Elizabeth H. & Pawar, Rajesh J., 2018. "Geologic CO2 sequestration monitoring design: A machine learning and uncertainty quantification based approach," Applied Energy, Elsevier, vol. 225(C), pages 332-345.

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