This study presents a workflow to predict the upscaled absolute
permeability of the rock core direct from CT images whose resolution are
not sufficient to allow direct pore-scale permeability computation. This
workflow exploits the deep learning technique with the data of raw CT
images of rocks and their corresponding permeability value obtained by
performing flow simulation on high resolution CT images. The
permeability map of a much larger region in the rock core is predicted
by the trained neural network. Finally, the upscaled permeability of the
entire rock core is calculated by the Darcy flow solver, and the results
showed a good agreement with the experiment data. This proposed
deep-learning based upscaling method allows estimating the permeability
of large-scale core samples while preserving the effects of fine-scale
pore structure variations due to the local heterogeneity.