Optimized History Matching with Stochastic Image Transforming of a Deltaic Reservoir

The focus of this paper is to demonstrate an integrated characterization approach for a complex deltaic reservoir by using petrophysical properties and dynamic production data. Accurately characterizing deltaic reservoirs requires a non-stationary approach to the reservoir description. By linking the non-stationary characterization step with the dynamic flow modeling, the probability of success in locating infill wells increases. In this work, is presented a hybrid method which integrates the optimization firstly in the space of the anisotropy model parameters and secondly refines it in the space of the static models with a regional perturbation technique. It is an iterative methodology for optimized history matching, using adaptive stochastic sampling in the multiparameter space, and direct sequential simulation as the engine for the image transformation of the porosity and permeability models of the reservoir. The results show coherence when compared with the true case model. The approach provides the simultaneous simulation of the morphology and the property value.