History matching using probabilistic approach in a distributed computing environment

The focus of the paper is to present a novel methodology for delineating multiple reservoir domains, for the purpose of history matching in a distributed computing environment. A fully probabilistic approach to perturb permeability within the delineated zones is implemented. The combination of robust schemes for identifying reservoir zones and distributed computing significantly increase the accuracy and efficiency of the probabilistic approach. The information pertaining to the permeability variations in the reservoir that is contained in dynamic data is calibrated in terms of a deformation parameter r_D. This information is merged with the prior geologic information in order to generate permeability models consistent with the observed dynamic data as well as the prior geology. The relationship between dynamic response data and reservoir attributes may vary in different regions of the reservoir due to spatial variations in reservoir attributes, well configuration, flow constrains etc. The probabilistic approach then has to account for multiple r_D values in different regions of the reservoir. In order to delineate reservoir domains that can be characterized with different r_D parameters, principal component analysis (PCA) of the Hessian matrix has been done. The Hessian matrix summarizes the sensitivity of the objective function at a given step of the history matching to model parameters. The eigenvectors obtained during the PCA are suitably scaled and appropriate grid block volume cut-offs are defined such that the resultant domains are neither too large (which increases interactions between domains) nor too small (implying ineffective history matching).Since the domain delineation process yields zones that are least correlated with each other, each r_D parameter can be optimized independently and simultaneously using individual nodes of a cluster of computers. Upon convergence, the perturbed regions are put together and the history match is verified. The proposed approach results in a set of independent tasks of equal magnitude and thus is particularly suited for distributed computing.