The relative roles of parameters governing relative permeability, a crucial property for two-phase fluid flows, are incompletely known. To characterize the influence of viscosity ratio (M) and capillary number (Ca), we calculated relative permeabilities of nonwetting fluids (knw) and wetting fluids (kw) in a 3D model of Berea sandstone under steady-state condition using the lattice Boltzmann method. We show that knw increases and kw decreases as M increases due to the lubricating effect, locally occurred pore-filling behavior, and instability at fluid interfaces. We also show that knw decreases markedly at low Ca (log Ca < −1.25), whereas kw undergoes negligible change with changing Ca. An M–Ca–knw correlation diagram, displaying the simultaneous effects of M and Ca, shows that they cause knw to vary by an order of magnitude. The color map produced is useful to provide accurate estimates of knw in reservoir-scale simulations and to help identify the optimum properties of the immiscible fluids to be used in a geologic reservoir.

Monitoring the hydraulic properties within subsurface fractures is vitally important in the contexts of geoengineering developments and earthquakes. Geophysical observations are promising tools for remote determination of subsurface hydraulic properties; however, quantitative interpretations are hampered by the paucity of relevant geophysical data for fractured rock masses. This study explored simultaneous changes in hydraulic and geophysical properties of natural rock fractures with increasing normal stress and correlated these property changes through coupling experiments and digital fracture simulations. We show that electrical resistivity is linked with permeability and flow area regardless of fracture roughness, whereas elastic wave velocity is roughness dependent. We also are able to categorize fracture flow patterns as aperture-dependent, aperture-independent, or disconnected flows, with transitions at specific stress levels. Elastic wave velocity offers potential for detecting the transition between aperture-dependent flow and aperture-independent flow, and resistivity is sensitive to detect the connection/disconnection of the fracture flow.

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.