SPE-104255-PA-P - Streamline-Assisted Ensemble Kalman...

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Streamline-Assisted Ensemble Kalman Filter for Rapid and Continuous Reservoir Model Updating Elkin Arroyo-Negrete, Deepak Devegowda, and Akhil Datta-Gupta, SPE, Texas A&M University, and J. Choe, SPE, Seoul National University Summary The use of the ensemble Kalman filter (EnKF) is a promising approach for data assimilation and assessment of uncertainties dur- ing reservoir characterization and performance forecasting. It pro- vides a relatively straightforward approach to incorporating di- verse data types, including production and/or time-lapse seismic data. Unlike traditional sensitivity-based history matching meth- ods, the EnKF relies on a cross-covariance matrix computed from an ensemble of reservoir models to relate reservoir properties to production data. For practical field applications, we need to keep the ensemble size small for computational efficiency. However, this leads to poor approximations of the cross-covariance and, often, loss of geologic realism through parameter overshoots, in particular by introducing localized patches of low and high per- meabilities. Because the EnKF estimates are “optimal” only for Gaussian variables and linear dynamics, these difficulties are com- pounded by the strong nonlinearity of the multiphase history matching problems and for non-Gaussian prior models. Specifi- cally, the updated parameter distribution tends to become multi- Gaussian with loss of connectivities of extreme values, such as high permeability channels and low permeability barriers, which are of special significance during reservoir characterization. We propose a novel approach to overcome some of these limi- tations by conditioning the cross-covariance matrix using informa- tion gleaned from streamline trajectories. Our streamline-assisted EnKF is analogous to the conventional assisted history matching, whereby the streamline trajectories are used to identify gridblocks contributing to the production response of a specific well. We then use these gridblocks only to compute the cross-covariance matrix and eliminate the influence of unrelated or distant observations and spurious correlations. We show that the streamline-assisted EnKF is an efficient and robust approach for history matching and con- tinuous reservoir model updating. We illustrate the power and utility of our approach using both synthetic and field applications. Introduction Proper characterization of the reservoir and the assessment of un- certainty are crucial aspects of any optimal reservoir development plan and management strategy. To achieve this goal, it is necessary to reconcile geological models to the dynamic response of the reservoir through history matching. The topic of history matching has been of great interest and an area of active research in the oil industry (Datta-Gupta and King 2007; Emanuel and Milliken 1998; Oliver et al. 2001). The past decade has seen some signif- icant developments in assisted and automatic history matching of high-resolution reservoir models and associated uncertainty quan- tification. Many of these techniques involve computation of sen-
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