Efficient deep-learning-based surrogate model for reservoir production optimization using transfer learning and multi-fidelity data

In the realm of subsurface flow simulations, deep-learning-based surrogate models have emerged as a promising alternative to traditional simulation methods, especially in addressing complex optimization problems. However, a significant challenge lies in the necessity of numerous high-fidelity training simulations to construct these deep-learning models, which limits their application to field-scale problems. To overcome this limitation, we introduce a training procedure that leverages transfer learning with multi-fidelity training data to construct surrogate models efficiently. The procedure begins with the pre-training of the surrogate model using a relatively larger amount of data that can be efficiently generated from upscaled coarse-scale models. Subsequently, the model parameters are finetuned with a much smaller set of high-fidelity simulation data. For the cases considered in this study, this method leads to about a 75% reduction in total computational cost, in comparison with the traditional training approach, without any sacrifice of prediction accuracy. In addition, a dedicated well-control embedding model is introduced to the traditional U-Net architecture to improve the surrogate model's prediction accuracy, which is shown to be particularly effective when dealing with large-scale reservoir models under time-varying well control parameters. Comprehensive results and analyses are presented for the prediction of well rates, pressure and saturation states of a 3D synthetic reservoir system. Finally, the proposed procedure is applied to a field-scale production optimization problem. The trained surrogate model is shown to provide excellent generalization capabilities during the optimization process, in which the final optimized net-present-value is much higher than those from the training data ranges. (c) 2025 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/ 4.0/).