An adaptive Kriging surrogate method for efficient uncertainty quantification with an application to geological carbon sequestration modeling

Mo, Shaoxing; Shi, Xiaoqing; Lu, Dan; Ye, Ming; Wu, Jichun

doi:10.1016/j.cageo.2019.01.012

Title: An adaptive Kriging surrogate method for efficient uncertainty quantification with an application to geological carbon sequestration modeling

Journal Article · Tue Jan 29 00:00:00 EST 2019 · Computers and Geosciences

DOI:https://doi.org/10.1016/j.cageo.2019.01.012· OSTI ID:1495937

^[1]; Shi, Xiaoqing ^[1];

^[2];

^[3]; Wu, Jichun ^[1]

Nanjing University (China)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Florida State Univ., Tallahassee, FL (United States)

We present that in numerical modeling of geological carbon sequestration (GCS), uncertainty quantification (UQ) is usually needed to evaluate the impact of uncertain model parameters on model predictions caused by limited measurements and incomplete knowledge of the parameters. However, UQ for GCS is computationally expensive due to the large ensemble of complex and lengthy model simulations. In this study, we propose an adaptive Kriging method to build a fast-to-evaluate surrogate of the GCS model to alleviate the heavy computational burden. The surrogate model is efficiently generated using a Taylor expansion-based adaptive experimental design algorithm that combines a distance-based exploration criterion and an exploitation criterion to adaptively search for informative training samples. In addition, we analyze the uncertainty brought by substituting the surrogate for the actual simulation model and explore its influence on UQ results. Lastly, our method is demonstrated in a synthetic GCS model and its performance is evaluated in comparison with the conventional Monte Carlo sampling. Results indicate that our method can greatly improve the computational efficiency in UQ and provide an effective and reliable UQ solution with the consideration of surrogate uncertainty.

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Research Organization:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1495937

Journal Information:: Computers and Geosciences, Vol. 125, Issue C; ISSN 0098-3004

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 13 works

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