IngentaConnect A Weighted Ensemble Kalman Filter for Automatic History Matching

Authors: Liang, B.¹; Sepehrnoori, K.¹; Delshad, M.¹

Source: Petroleum Science and Technology, Volume 27, Number 10, January 2009 , pp. 1062-1091(30)

Abstract:

The ensemble Kalman filter (EnKF) performs the initial sampling, forecasting, and assimilation steps for automatic history matching in the petroleum industry. It tunes multiple members sequentially and updates the statistical mean and variance of the model. Many applications have been reported in various publications. The forecasting step is implemented by running the reservoir model simulator. In the assimilation equation, the ensemble mean is calculated through equally weighting all the members. Therefore, the contribution factor to the mean from each member is the same. This paper proposes a modified assimilation equation by introducing a weighting factor for each ensemble member. Both the proposed weighted EnKF and the traditional EnKF are applied to a modified field case of a complex seventeen-layer reservoir. The performances of the weighted EnKF on production history match, forecasting, and field permeability match are better than those from the traditional EnKF. In addition, we investigate the impact of geological uncertainty in the initial ensemble generation on the final matching results. Two scenarios which have the same semivariogram as the reference field are implemented, and their results show that the initial geological information is important to the history matching performance.

Keywords: ensemble Kalman filter; geological uncertainty; history matching; weighted

Document Type: Research article

DOI: 10.1080/10916460802455939

Affiliations: 1: Department of Petroleum and Geosystems Engineering, The University of Texas at Austin, Austin, TX, USA

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