Multiscale Reservoir Simulation Through Super-Resolution Techniques

Abstract High-resolution reservoir simulation plays a vital role in understanding subsurface flow behavior and optimizing reservoir management strategies. However, the computational cost of fine-scale simulations poses a significant challenge, particularly for large-scale reservoirs and uncertainty quantification studies. Traditional upscaling methods often struggle to conserve critical fine-scale static and flow dynamic features, while recent super-resolution techniques in deep learning lack physical consistency in their predictions. To address these limitations, this paper presents a novel physics-constrained super-resolution framework that efficiently downscales coarse-scale simulation predictions while ensuring the physical consistency. Our framework employs a generative adversarial network (GAN) architecture specifically designed for reservoir simulation, featuring multi-input integration of dynamic flow information and static reservoir properties. The network is trained with multiple loss functions through a two-phase strategy, combining traditional reconstruction metrics with physical constraints. The framework processes pressure and saturation fields along with well information, achieving PSNR values of 35.52 dB and 31.63 dB for pressure and saturation fields respectively, with corresponding SSIM values of 0.9969 and 0.9797. Detailed analysis reveals the performance in well regions, with R2 values of 0.9163 for pressure and 0.9875 for water saturation predictions at well locations, though challenges remain in capturing extreme pressure values near wells and sharp saturation transitions at flood fronts. The framework's ability to maintain high accuracy while significantly reducing computational requirements makes it a promising tool for practical reservoir management applications, offering a balanced solution to the long-standing trade-off between computational efficiency and simulation accuracy.