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Multimodal ensemble Kalman filtering using Gaussian mixture models

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Abstract

In this paper we present an extension of the ensemble Kalman filter (EnKF) specifically designed for multimodal systems. EnKF data assimilation scheme is less accurate when it is used to approximate systems with multimodal distribution such as reservoir facies models. The algorithm is based on the assumption that both prior and posterior distribution can be approximated by Gaussian mixture and it is validated by the introduction of the concept of finite ensemble representation. The effectiveness of the approach is shown with two applications. The first example is based on Lorenz model. In the second example, the proposed methodology combined with a localization technique is used to update a 2D reservoir facies models. Both applications give evidence of an improved performance of the proposed method respect to the EnKF.

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Authors and Affiliations

  1. Eni Exploration & Production, San Donato Milanese, Milan, Italy

    Laura Dovera & Ernesto Della Rossa

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  1. Laura Dovera
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  2. Ernesto Della Rossa
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Correspondence to Laura Dovera.

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Dovera, L., Della Rossa, E. Multimodal ensemble Kalman filtering using Gaussian mixture models. Comput Geosci 15, 307–323 (2011). https://doi.org/10.1007/s10596-010-9205-3

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