A methodology to construct deep neural network- (DNN) and recurrent neural network- (RNN) based proxy flow models is presented; these can reduce computational time of the flow simulation runs in the routine reservoir engineering workflows, such as history matching or optimization. A comparison of these two techniques shows that the DNN model generates predictions more quickly, but the RNN model provides better quality. In addition, RNN-based proxy flow models can make predictions for times after those included in the training data set. Both approaches can reduce computational time by a factor of up to 100 in comparison to the full-physics flow simulator. An example of the proxy flow model application is successfully demonstrated in an exhaustive search history matching exercise. All developments are shown on a synthesized Brugge petroleum reservoir.

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