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      Data Assimilation for a Geological Process Model Using the Ensemble Kalman Filter

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          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 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.

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          Most cited references 13

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          Quantitative models of sedimentary basin filling

           Chris Paola (2000)
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            Ensemble smoother with multiple data assimilation

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              A Bayesian approach to inverse modelling of stratigraphy, part 1: method

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                Author and article information

                Journal
                21 November 2017
                Article
                1711.07763

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                Custom metadata
                34 pages, 10 figures, 4 tables
                stat.AP

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