Gradient based optimization techniques in computer-aided history matching are increasingly adopted by oil industries, because of the great time saving they can offer over conventional trial and error approaches. However, these methods lead to the identification of a single set of parameters, thus neglecting the inherent non-uniqueness of the solution of the underlying inverse problem. In this paper we propose a new approach that couple a chaotic sampling of parameter space with a local minimization technique. The first step is effected evolving a non-linear dynamical system, by which we identify several points to be successively used as initial guesses for a local, gradient based, optimizer. This provides a series of alternative matched models, with different production forecasts, that improve the understanding of the possible reservoir behaviors. The validity of this approach has been proven on a synthetic reservoir derived from a real West Africa field.

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