History Matching a Field Case Using the Ensemble Kalman Filter With Covariance Localization
- Alexandre A. Emerick (University of Tulsa) | Albert C. Reynolds (University of Tulsa)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- August 2011
- Document Type
- Journal Paper
- 423 - 432
- 2011. Society of Petroleum Engineers
- 5.5.8 History Matching, 5.1 Reservoir Characterisation
- History matching, Covariance localization, Ensemble Kalman filter, Field case
- 2 in the last 30 days
- 799 since 2007
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Because of its ease of implementation, computational efficiency, and the fact that it generates multiple history-matched models, which conceptually allows one to characterize the uncertainty in reservoir description and future performance predictions, the ensemble Kalman filter (EnKF) provides a highly attractive technique for history matching production data. In this work, we apply EnKF with a recently proposed method of covariance localization to history match production data from a real field to generate multiple realizations of the permeability field. A single manually history-matched model is available for comparisons. Only 7.6 years of the 10 years of history were matched, with the remaining 2.4 years of history used to assess the predictive capability of the history-matched models. For this field case, covariance localization was necessary to avoid the propagation of spurious correlations and loss of variance and also resulted in better data matches and predictions than were obtained with EnKF without localization. EnKF with covariance localization also gave better data matches, more-accurate "future" predictions, and far more geologically realistic models than were obtained by manually matching production data. We also present results obtained using half-iteration EnKF (HI-EnKF) with covariance localization. For this field case, HI-EnKF gave a significant further improvement in the data match and predictions. However, because HI-EnKF requires rerunning the ensemble from time zero at every data-assimilation step, it leads to a considerable increase in the computational time. The results for this field case indicate that we can reduce the computational cost of HI-EnKF, without compromising the quality of the results, by rerunning the ensemble from time zero only when "large" changes in the state vector occur.
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