Reservoir Simulation

Reservoir simulation aims to predict fluid flow in underground reservoirs, crucial for optimizing oil and gas extraction and managing subsurface storage (e.g., CO2 sequestration). Current research heavily emphasizes developing faster and more accurate surrogate models using machine learning, particularly deep learning architectures like convolutional neural networks, recurrent neural networks (especially ConvLSTMs), and graph neural networks, often incorporating physics-informed approaches to improve accuracy and generalizability. These advancements significantly accelerate computationally expensive simulations, enabling more efficient reservoir management, history matching, and optimization of well placement and control strategies.