Local adaptive parameterization for the history matching of 3D seismic data

Abstract

Matching seismic data in assisted history matching processes can be a challenging task. One main idea is to bring flexibility in the choice of the parameters to be perturbed, focusing on the information provided by seismic data. Local parameterization techniques such as pilot-point or gradual deformation methods can be introduced, considering their high adaptability. However, the choice of the spatial supports associated to the perturbed parameters is crucial to successfully reduce the seismic mismatch. The information related to seismic data is sometimes considered to initialize such local methods. Recent attempts to define the regions adaptively have been proposed, focusing on the mismatch between simulated and reference seismic data. However, the regions are defined manually for each optimization process. Therefore, we propose to drive the definition of the parameter support by performing an automatic definition of the regions to be perturbed from the residual maps related to the 3D seismic data. Two methods are developed in this paper. The first one consists in clustering the residual map with classification algorithms. The second method proposes to drive the generation of pilot point locations in an adaptive way. Residual maps, after proper normalization, are considered as probability density functions of the pilot point locations. Both procedures lead to a complete adaptive and highly flexible perturbation technique for 3D seismic matching. A synthetic study based on the PUNQ test case is introduced to illustrate the potential of these adaptive strategies.

Subdomain POD-TPWL with Local Parameterization for Large-Scale Reservoir History Matching Problems

“…From a computational point of view, a local parameterization where the parameters are separately defined in each subdomain is very attractive [14]. Since in this case we can mainly focus on the independent target zones, and parameters in each subdomain can be perturbed simultaneously of each other and the effects of all these perturbations can be computed with only a few full-order model simulations [15,16].…”