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Field-of-view and resolution trade-offs in x-ray micro-computed-tomography (micro-CT) imaging limit the characterization, analysis, and model development of multiscale porous systems. To this end, we develop an applied methodology utilizing deep learning to enhance low-resolution (LR) images over large sample sizes and create multiscale models capable of accurately simulating experimental fluid dynamics from the pore (microns) to continuum (centimeters) scale. We develop a three-dimensional (3D) enhanced deep-superresolution (EDSR) convolutional neural network to create superresolution (SR) images from LR images, which alleviates common micro-CT hardware and/or reconstruction defects in high-resolution (HR) images. When paired with pore-network simulations and parallel computation, we can create large 3D continuum-scale models with spatially varying flow and material properties. We quantitatively validate the workflow at various scales using direct HR and SR image similarity, pore-scale material and/or flow simulations, and continuum-scale multiphase-flow experiments (drainage-immiscible flow pressures and 3D fluid-volume fractions). The SR images and models are comparable to the HR ground truth and generally accurate to within experimental uncertainty at the continuum scale across a range of flow rates. They are found to be significantly more accurate than their LR counterparts, especially in cases where a wide distribution of pore sizes are encountered. The applied methodology opens up the possibility to image, model, and analyze truly multiscale heterogeneous systems that are otherwise intractable.
Jackson et al. (Fri,) studied this question.