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 fine-tuned with a much smaller set of high-fidelity simulation data. For the cases considered in thi…