An Improved Inversion Workflow Jointly Assimilating 4D Seismic and Production Data

Abstract

Abstract Quantitative integration of 4D seismic data with production data into reservoir models is a challenging task. One important issue is how to properly quantify the uncertainty, or the posterior probability distribution (PPD). The Very Fast Simulated Annealing (VFSA) is a stochastic searching method, whereas the Simultaneous Perturbation and Multivariate Interpolation (SPMI) is a model-based local searching method. The stochastic features of the VFSA provide the feasibility of identifying possible multiple peaks of a PPD, but it converges very slowly. On the other hand, the model-based SPMI method has the advantages of effectively utilizing the smooth features of an objective function, and thus can converge to local optimum very quickly. More importantly, the Hessian of the objective function, or the covariance matrix of the PPD, can be estimated by the SPMI method with satisfactory accuracy. However, it is very difficult to identify multiple optima by applying the SPMI method alone. In this paper, we propose an efficient joint inversion workflow by appropriately integrating the two derivative free optimization (DFO) methods. The complementary features of the two methods can further improve both applicability and efficiency of this joint inversion workflow. We tested the workflow with a 3D synthetic model and a real field case. Our results show that the integrated method is efficient and can deliver good results for jointly assimilating 4D seismic and production data.