Ninth International Geostatistics Congress, Oslo, Norway
June 11 – 15, 2012


Plenary 4

Abstract No.:



Updating multipoint simulations using the ensemble Kalman filter


L.Y. Hu, ConocoPhillips (US)
Y. Zhao, ConocoPhillips (US)
Y. Liu, ConocoPhillips (US)
C. Scheepens, ConocoPhillips (US)
A. Bouchard, ConocoPhillips (US)


In the last two decades, the multiple-point simulation (MPS) method has been developed and increasingly used for building complex geological facies models that are conditioned to geological and geophysical data. In the meantime, the ensemble Kalman filter (EnKF) approach has been developed and recognized as a promising way for conditioning reservoir models to production history and for uncertainty quantification. So far, the EnKF approach is proven efficient for updating continuous model parameters that have a linear statistical relation with the flow responses. It remains challenging to extend the EnKF approach to updating complex geological facies models generated by MPS. In this paper, we first propose a new parameterization method to represent the MPS facies models with a set of continuous parameters. It is mathematically proven that, by changing or optimizing these parameters, we maintain the geological and statistical consistency of the MPS facies models. Then, we associate the above parameterization method with the EnKF approach for dynamic data integration. This association extends the potential applicability of the EnKF to complex geological facies models generated by MPS. Finally, we present encouraging results of using the above methodology to condition a synthetic fluvial reservoir model to dynamic data.




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