Application of an Improved Deep-Learning Framework for Large-Scale Subsurface Flow Problems with Varying Well Controls

Abstract

Summary The embed-to-control (E2C) framework provides a new deep-learning-based reduced-order modeling framework for much faster subsurface flow predictions than traditional simulation. However, the previous E2C model entails a large number of model parameters, which limits its applicability to large-scale cases. In addition, the previous E2C model has not been applied to a gas-driven subsurface system or well-control optimization. In this work, we make several improvements to the previous E2C framework for more complex and larger-scale problems. First, we reduce the output dimension of the middle layers by increasing the number of downsampling layers and using the depth-wise separable (DWS) convolution techniques in the deconvolution operation. Second, we use the global average pooling (GAP) technique to reduce the model parameters. Third, we apply an “add” operation in the skip connection to fuse the features. The improved E2C surrogate model is applied to a high-dimensional gas system with flow driven by six wells operating under time-varying control specifications. In this case, we can reduce the graphics processing unit (GPU) memory usage from 19.22 GB to 2.57 GB. In the training process, a total of 160 high-fidelity simulations are performed offline, out of which 130 simulation results with partial time sequence are used for training the E2C surrogate model, which takes about 46 hours on an RTX 3090 GPU. The trained model is shown to provide accurate production forecasts under various well control scenarios during the prediction period. The online computations from our E2C model are about 6.5 seconds per case, which achieves a speedup of more than 500 factors to corresponding full-order simulations, which take about 1 hour per run. Finally, the improved E2C model, in conjunction with a particle swarm optimization (PSO) technique, is applied to optimize the injection well strategies of an oil-gas-water field case with 189 wells (i.e., 96 producers and 93 injectors). Due to the significant speedup and high accuracy of the improved surrogate model, it is shown that improved well-control strategies can be efficiently obtained.