Assisted history matching is widely used to constrain reservoir models by integrating well production data and/or 4D seismic data. However, history matching is a complex inverse problem, and the computational effort increases with the increasing of the number of matching parameters. It is always a big challenge to history match large fields with a large number of parameters.

In history matching, the objective function can be separated into local components referred to the wells and/or the seismic zones, and a local component generally depends on a smaller number of principal parameters. This partial separability of the objective function helps to compute derivatives with a smaller number of reservoir simulations for the gradient-based optimization methods. In this paper, we present a new technique for the derivative computation and discuss how to generate an "optimal" perturbation design for a partially separable objective function. The proposed method can decrease the objective function with much fewer simulations in history matching.

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