Reservoir Surrogate Modeling Using U-Net with Vision Transformer and Time Embedding

Accurate and efficient modeling of subsurface flow in reservoir simulations is essential for optimizing hydrocarbon recovery, enhancing water management strategies, and informing critical decision-making processes. However, traditional numerical simulation methods face significant challenges due to their high computational cost and limited scalability in handling large-scale models with uncertain geological parameters, such as permeability distributions. To address these limitations, we propose a novel deep learning-based framework leveraging a conditional U-Net architecture with time embedding to improve the efficiency and accuracy of reservoir data assimilation. The U-Net is designed to train on permeability maps, which encode the uncertainty in geological properties, and is trained to predict high-resolution saturation and pressure maps at each time step. By utilizing the saturation and pressure maps from the previous time step as inputs, the model dynamically captures the spatiotemporal dependencies governing multiphase flow processes in reservoirs. The incorporation of time embeddings enables the model to maintain temporal consistency and adapt to the sequential nature of reservoir evolution over simulation periods. The proposed framework can be integrated into a data assimilation loop, enabling efficient generation of reservoir forecasts with reduced computational overhead while maintaining high accuracy. By bridging the gap between computational efficiency and physical accuracy, this study contributes to advancing the state of the art in reservoir simulation. The model’s ability to generalize across diverse geological scenarios and its potential for real-time reservoir management applications, such as optimizing production strategies and history matching, underscores its practical relevance in the oil and gas industry.