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Pore‐scale digital images are usually obtained from microcomputed tomography data that has been segmented into void and grain space. Image segmentation is a crucial step in the process of digital rock analysis that can influence pore‐scale characterization studies and/or the numerical simulation of petrophysical properties. This is concerning since all segmentation methods have user‐selected parameters that result in biases. Convolutional neural networks (CNNs) provide a way forward since once trained, CNN can provide consistent and reliable image segmentation with no user‐defined inputs. In this paper, a CNN is used to segment digital sandstone data, and various ground truth data sets are tested. The ground truth images are created based on high‐resolution microcomputed tomography data and corresponding scanning electron microscope data. The results are evaluated in terms of porosity, permeability, and pore size distribution computed from the segmented data. We find that watershed‐based segmentation provides a wide range of possible petrophysical values depending on user‐selected thresholds, whereas CNN provides a smaller variance when trained on scanning electron microscope data. It can be concluded that CNN offers a reliable and consistent way to segment digital sandstone data for petrophysical analyses.

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Wetting phenomena play a key role in flows through porous media. Relative permeability and capillary pressure-saturation functions show a high sensitivity to wettability, which has different definitions at the continuum-and pore-scale. At the continuum-scale, the state of wetting is defined as Amott-Harvey or USBM (United States Bureau of Mines) indices by capillary pressure drainage and imbibition cycles. At the pore-scale, the concept of contact angle is used, which until recently was not experimentally possible to determine within an opaque porous medium. Recent progress on measurements of pore-scale contact angles by X-ray computed micro-tomography has therefore attracted significant attention in various research communities. In this work, the Gauss-Bonnet theorem is applied to provide a direct link between capillary pressure saturation (P c (S w )) data and measured distributions of pore-scale contact angles. We propose that the wetting state of a porous medium can be described in terms of geometrical arguments that constrain the morphological state of immiscible fluids. The constraint describes the range of possible contact angles and interfacial curvatures that can exist for a given system. We present measurements in a tested sandstone for which the USBM index, P c (S w ), and pore-scale contact angles are measured. Additional studies are also performed using two-phase Lattice Boltzmann simulations to test a wider range of wetting conditions. We show that mean pore-scale contact angle measurements can be predicted from petrophysical data within a few differences. This provides a general framework on how continuum-scale data can be used to describe the geometrical state of fluids within porous media.