Assisted history matching using artificial neural network based global optimization method – Applications to Brugge field and a fractured Iranian reservoir

Abstract

Abstract Reservoir simulation is a powerful predictive tool used in reservoir management. Constructing a simulation model involves subsurface uncertainties which can greatly affect prediction results. Quantifying such uncertainties for a field under development necessitates history matching that is a difficult inverse problem with non-unique solutions. History matching is used to minimize the difference between the observed field data and the simulation results and requires numerous simulation runs. In many engineering simulation-based optimization problems, the number of function evaluations is a prohibitive factor limited by time or cost. History matching in hydrocarbon reservoir simulation is one of such computationally expensive problems which pose challenges in the field of global optimization. One way to overcome this difficulty is to use an artificial neural network (ANN) as a surrogate model. This article presents an ANN-based global optimization method that is used for history matching problem. The method has been applied to an Iranian fractured oil reservoir and the famous Brugge field benchmark. Computational results confirm the success of this method in history matching. We compare history matching results obtained by the proposed method with those of manual history matching and those obtained by simulation based direct optimization algorithm. The results compares favourably with manual history matching in terms of matching quality. The proposed method is superior than the simulation based direct optimization algorithm in finding multiple matched scenarios in less computation time.