In this paper we present a new multiscale approach to history matching assisted by a neural network metamodel. The method starts with the construction of a fine scale a priori model that includes geological and geostatistical information. We then apply a singular value decomposition (SVD) in order to obtain a parametric representation of the permeability field, in a way that a fixed set of eigenimages are determined with the parameters to be inverted as weights in the expansion. Through this procedure not only is the number of parameters significantly reduced, but also the weights in the SVD expansion define a hierarchy that naturally separates the different resolution scales in the system. We show that the multiscale procedure alone helps to significantly reduce the CPU time required to accomplish the parameter estimation. Furthermore, the reduced parameter space facilitated the training of the neural network engine.

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