Efficient Inverse Modeling Using Probabilistic Sensitivity Analysis with Gradual Deformation Algorithm

Abstract

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One of the challenging topics in inverse modeling is numerical efficiency and efficient optimization algorithms have been researched. In optimization algorithms, we need efficient perturbations to find a global minimum. Geostatistical methods combined with perturbation have an important role in generating input parameter fields. However, in cases where the given static sample data are not pertinent considering dynamic data, it is difficult to find a global minimum in the inverse modeling. To overcome this problem, we propose the concept of probabilistic sensitivity analysis. From the sensitivity analysis, cumulative probability distribution functions, which are used as indicators of perturbations, are constructed. Then, they are used for field generations in the gradual deformation method. By enforcing the restart of the optimization process at a local minimum state, the inverse modeling becomes stable and efficient. From the applications using various synthetic reservoirs, the proposed scheme works even in cases where reservoirs have hidden abnormal permeability values, which are not represented by sampling data.