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Pore-scale imaging and modeling has advanced greatly through the integration of Deep Learning into the workflow, from image processing to simulating physical processes. In Digital Core Analysis, a common tool in Earth Sciences, imaging the nano- and micro-scale structure of the pore space of rocks can be enhanced past hardware limitations, while identification of minerals and phases can be automated, with reduced bias and high physical accuracy. Traditional numerical methods for estimating petrophysical parameters and simulating flow and transport can be accelerated or replaced by neural networks. Techniques and common neural network architectures used in Digital Core Analysis are described with a review of recent studies to illustrate the wide range of tasks that benefit from Deep Learning. Focus is placed on the use of Convolutional Neural Networks (CNNs) for segmentation in pore-scale imaging …