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Subsurface fluid flow, essential in various natural and engineered processes,is largely governed by a rock's permeability, which describes its ability toallow fluid passage. While convolutional neural networks (CNNs) have beenemployed to estimate permeability from high-resolution 3D rock images, ournovel visualization technology reveals that they occasionally miss higher-levelcharacteristics, such as nuanced connectivity and flow paths, within porousmedia. To address this, we propose a novel fusion model to integrate CNN withthe graph neural network (GNN), which capitalizes on graph representationsderived from pore network model to capture intricate relational data betweenpores. The permeability prediction accuracy of the fusion model is superior tothe standalone CNN, whereas its total parameter number is nearly two orders ofmagnitude lower than the latter. This innovative approach not only heralds anew frontier in the research of digital rock property predictions, but alsodemonstrates remarkable improvements in prediction accuracy and efficiency,emphasizing the transformative potential of hybrid neural network architecturesin subsurface fluid flow research.

A Computationally Efficient Hybrid Neural Network Architecture for Porous Media: Integrating CNNs and GNNs for Improved Permeability Prediction