Abstract
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. 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.
Original language | English |
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Pages (from-to) | 69-77 |
Number of pages | 9 |
Journal | Computers and Geosciences |
Volume | 125 |
DOIs | |
State | Published - Apr 2019 |
Funding
We are grateful to the High Performance Computing Center of Nanjing University for doing the numerical calculations in this manuscript on its blade cluster system. This work was financially supported by the National Key Research and Development Program of China (No. 2018YFC0406402 ) and the National Nature Science Foundation of China grants (No. 41672229 and U1503282 ). We are grateful to the High Performance Computing Center of Nanjing University for doing the numerical calculations in this manuscript on its blade cluster system. This work was financially supported by the National Key Research and Development Program of China (No. 2018YFC0406402) and the National Nature Science Foundation of China grants (No. 41672229 and U1503282).
Funders | Funder number |
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National Nature Science Foundation of China | U1503282, 41672229 |
Natural Science Foundation of Hubei Province | |
National Basic Research Program of China (973 Program) | 2018YFC0406402 |
Keywords
- Adaptive experimental design
- Geological carbon sequestration
- Kriging
- Surrogate modeling
- Uncertainty quantification