Data assimilation for a geological process model using the ensemble Kalman filter

• Published in: Basin Research
• Main Authors: Skauvold, Jacob, Eidsvik, Jo
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2017
• Subjects: north slope; Alaska
• 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

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.