2017/11/21 by Jacob Skauvold, Jo Eidsvik, Skauvold, Jacob +1
Earth and Planetary Sciences · Engineering · Environmental Science · #Applications (stat.AP) #FOS: Computer and information sciences #Geological Modeling and Analysis #Reservoir Engineering and Simulation Methods #Soil Geostatistics and Mapping
paper · pdf · doi:10.48550/arxiv.1711.07763
openalex publication_date 2017/11/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 characterize 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.