Reservoir Pressure

Reservoir pressure prediction is crucial for optimizing oil and gas extraction, carbon sequestration, and geothermal energy projects, aiming to improve resource management and reduce economic risks. Current research heavily utilizes machine learning, employing architectures like neural networks (including physics-informed and graph neural networks), autoregressive models, and transformers to forecast pressure, production rates, and other reservoir properties from diverse data sources (seismic data, well logs, and surface displacement). These advanced modeling techniques offer significant improvements in prediction accuracy and computational speed compared to traditional methods, enabling more efficient reservoir management and risk assessment.