Advanced reservoir management workflow using an EnKF based assisted history matching method

TitleAdvanced reservoir management workflow using an EnKF based assisted history matching method
Publication TypeConference Proceedings
Year of Publication2009
AuthorsSeiler, A, Evensen, G, Skjervheim, J-A, Hove, J, Vabø, JG
Refereed DesignationUnknown
Conference NameThe 2009 SPE Reservoir Simulation Symposium, 65533
Series/Publication TitleSPE 118906
PublisherSociety of Petroleum Engineers, Inc.
Conference Location and DateThe Woodlands, Texas, USA, 2-4 February 2009

This paper demonstrates the potential and advantages of the Ensemble Kalman filter (EnKF) as a tool for assisted history matching, based on its sequential processing of measurements, its capability of handling large parameter sets, and on the fact that it solves the combined state and parameter estimation problem. A method and a thorough workflow for updating reservoir simulation models using the EnKF is developed. In addition, we present a method for updating relative permeability curves, as well as an improved approach for updating fault transmissibility multipliers. The proposed workflow has been applied on a complex North Sea oil field. The EnKF successfully provides an ensemble of history matched reservoir models. A significant improvement in the history match is obtained by updating the relative permeability properties in addition to porosity and permeability fields and initial fluid contacts. Fault multipliers are estimated, and it is shown how the use of transformations, which handles non-Gaussian model variables, makes it possible to determine if a fault is open, closed, or partially closed with respect to flow. The presented method is an innovative contribution to reservoir management workflows, which show growing interest in real time applications and fast model updating. Sequential data assimilation provides an updated reservoir model conditioned on the most recent production data. The updated ensemble is used to predict the uncertainty in future production and it is demonstrated that the EnKF leads to improved predictions with reduced uncertainty.

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