MicroGraphNets: Automated characterization of the micro-scale wettability of porous media using graph neural networks
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Abstract
This study introduces MicroGraphNets, a deep learning framework for automating the microscopic characterization of wettability in porous media using graph neural networks. The framework predicts rock surface roughness, fluid/fluid interfacial curvatures, and contact angles at 3-phase contact lines from segmented multiphase micro-computed tomography images. This is achieved by converting these images into sets of surface and interfacial points, with their intersection defining the 3-phase contact line points. Specialized geometrical training graphs are constructed from these points to predict each property, leveraging surface and interfacial normal vectors as input features for constructing surface and interfacial graphs. To address the unique challenge that arises from the coexistence of all phases around 3-phase contact lines, distinct node types assigned to each phase were embedded as node features for constructing contact angle graphs. To predict the properties, the framework employs a message-passing graph neural network with three modules: an encoder for initial feature embeddings, a processor for aggregating neighboring embeddings and propagating messages, and a decoder for final property prediction. This approach effectively captures node and edge relationships, facilitating accurate regression of surface and interfacial properties. Validation includes testing on unseen samples and a synthetic droplet test against analytical solutions. Time-resolved analysis was performed to demonstrate the scalability and efficiency of the framework on large datasets. MicroGraphNets demonstrates superior accuracy and efficiency compared to traditional deep learning methods, showcasing its potential for predicting microscopic surface and interfacial properties of porous media.
Document Type: Original article
Cite as: Alzahrani, M. K., Shapoval, A., Chen, Z., Rahman, S. S. MicroGraphNets: Automated characterization of the micro-scale wettability of porous media using graph neural networks. Capillarity, 2024, 12(3): 57-71. https://doi.org/10.46690/capi.2024.09.01
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