Data assimilation for a geological process model using the ensemble Kalman filter
Abstract We consider the problem of conditioning a geological process‐based computer simulation, which produces basin models by simulating transport and deposition of sediments, to data. Emphasising uncertainty quantification, we frame this as a Bayesian inverse problem, and propose to characterise...
| Published in: | Basin Research |
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| Main Authors: | , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Wiley
2017
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| Subjects: | |
| Online Access: | http://dx.doi.org/10.1111/bre.12273 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fbre.12273 https://onlinelibrary.wiley.com/doi/pdf/10.1111/bre.12273 |
| Summary: | Abstract We consider the problem of conditioning a geological process‐based computer simulation, which produces basin models by simulating transport and deposition of sediments, to data. Emphasising uncertainty quantification, we frame this as a Bayesian inverse problem, and propose to characterise the posterior probability distribution of the geological quantities of interest by using a variant of the ensemble Kalman filter, an estimation method which linearly and sequentially conditions realisations of the system state to data. A test case involving synthetic data is used to assess the performance of the proposed estimation method, and to compare it with similar approaches. We further apply the method to a more realistic test case, involving real well data from the Colville foreland basin, North Slope, Alaska. |
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