Integrating dynamic data in reservoir models using a parallel computational approach

Conditioning reservoir models to dynamic data is challenging due to the non-linear relationship between the measured flow response data and the model parameters (porosity, permeability etc.). The focus of this paper is to present a methodology for efficiently integrating dynamic production data into reservoir models. In contrast to other methods for production data integration, the proposed methodology attempts to quantify the information in production data pertaining to reservoir heterogeneity in a probabilistic manner. The conditional probability representing the uncertainty in permeability at a location is iteratively updated to account for the additional information contained in the dynamic response data. A localized perturbation procedure is also presented to account for multiple flow regions within the reservoir. The proposed methodology is demonstrated on a realistic case example. The methodology for implementing the proposed algorithm on parallel cpu's is presented. The computational synergies realized through domain decomposition flow simulation are likely to result in significant cpu savings.