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History-matching of petroleum reservoir models by the ensemble Kalman filter and parameterization methods

Abstract : History-matching enables integration of data acquired after the production in the reservoir model building workflow. Ensemble Kalman Filter (EnKF) is a sequential assimilation or history-matching method capable of integrating the measured data as soon as they are obtained. This work is based on the EnKF application for History-matching purposes and is divided into two main sections. First section deals with the application of the EnKF to several case studies in order to better understand the merits and shortcomings of the method. These case studies include two synthetic case studies (a simple one and a rather complex one), a Facies model and a real reservoir model. In most cases the method is successful in reproducing the measured data. The encountered problems are explained and possible solutions are proposed. Second section deals with two newly proposed algorithms combining the EnKF with two parameterization methods: pilot point method and gradual deformation method, which are capable of preserving second order statistical properties (mean and covariance). Both developed algorithms are applied to the simple synthetic case study. For the pilot point method, the application was successful through an adequate number and proper positioning of pilot points. In case of the gradual deformation, the application can be successful provided the background ensemble is large enough. For both cases, some improvement scenarios are proposed and further applications to more complex scenarios are recommended.
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Submitted on : Wednesday, March 30, 2011 - 10:35:16 AM
Last modification on : Wednesday, November 17, 2021 - 12:31:19 PM
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  • HAL Id : pastel-00581082, version 1


Leila Heidari. History-matching of petroleum reservoir models by the ensemble Kalman filter and parameterization methods. Earth Sciences. École Nationale Supérieure des Mines de Paris, 2011. English. ⟨NNT : 2011ENMP0006⟩. ⟨pastel-00581082⟩



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