Comparing the adaptive Gaussian mixture filter with the ensemble Kalman filter on synthetic reservoir models |
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Authors: | Andreas S Stordal Randi Valestrand Hans Arnfinn Karlsen Geir N?vdal Hans Julius Skaug |
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Institution: | 1. International Research Institute of Stavanger, Thorm?hlensgate 55, 5008, Bergen, Norway 2. Department of Mathematics, University of Bergen, Johs. Brunsgt. 12, P.O. Box 7803, 5020, Bergen, Norway
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Abstract: | Over the last years, the ensemble Kalman filter (EnKF) has become a very popular tool for history matching petroleum reservoirs.
EnKF is an alternative to more traditional history matching techniques as it is computationally fast and easy to implement.
Instead of seeking one best model estimate, EnKF is a Monte Carlo method that represents the solution with an ensemble of
state vectors. Lately, several ensemble-based methods have been proposed to improve upon the solution produced by EnKF. In
this paper, we compare EnKF with one of the most recently proposed methods, the adaptive Gaussian mixture filter (AGM), on
a 2D synthetic reservoir and the Punq-S3 test case. AGM was introduced to loosen up the requirement of a Gaussian prior distribution
as implicitly formulated in EnKF. By combining ideas from particle filters with EnKF, AGM extends the low-rank kernel particle
Kalman filter. The simulation study shows that while both methods match the historical data well, AGM is better at preserving
the geostatistics of the prior distribution. Further, AGM also produces estimated fields that have a higher empirical correlation
with the reference field than the corresponding fields obtained with EnKF. |
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