Permeability Field

Permeability field characterization aims to accurately represent the spatial distribution of fluid flow capacity within porous media, crucial for applications like reservoir management and CO2 storage. Current research heavily utilizes machine learning, employing architectures like convolutional neural networks, recurrent neural networks, and graph neural networks to create fast and accurate surrogate models for computationally expensive simulations. These models improve predictions of pressure and saturation, enabling efficient optimization of well placement and control strategies, and facilitating robust uncertainty quantification in inverse problems. The resulting advancements significantly accelerate reservoir characterization and optimization processes, leading to improved resource management and reduced environmental risk.