Data assimilation fora geological processmodel using the ensemble Kalman filter
J. Skauvold and J. Eidsvik
Journal name: Basin Research
Issue: Vol 30, No 4, August 2018 pp. 730 - 745
Info: Article, PDF ( 2.15Mb )
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.