Unprecedented development in digital rock physics has taken place in the past 2 decades supported by the advancement of imaging technology and contemporary computational capacity. Access to pore-scale morphology and connectivity of porous media provides the basis for accurate derivation of transport properties at the micro-scale (C. H. Arns et al., 2004;Blunt et al., 2013;Sakellariou et al., 2007), and promotes digital core analysis to be a robust numerical approach for reservoir characterization with rapid adoption worldwide. While digital core analysis provides an excellent toolset to detect and characterize small-scale heterogeneity, integration of digital core analysis into standard industry workflows poses the challenge of upscaling properties to well-log resolutions. The resolution gap between pore-scale and log-resolution (from micrometer to centimeter) is large and can be narrowed by considering micro-CT images at multiple resolutions (a few micrometers to over 50 micrometers) and consequently multiple field-of-views (FoV), typically from a few millimeters to 1 inch in diameter of the target samples.Integrating micro-CT images at multiple resolutions however poses unique challenges. While at low resolution the FoV is large and consequently larger-scale heterogeneity can be covered, the resultant resolution per voxel is limited, leading to significant changes in observed morphological measures (C. H. Arns et al., 2010). Many voxels will exhibit intermediate intensity since they represent mixtures between multiple phases, for example, pore space and solid. For those voxels phase morphology including connectivity is not directly observable and calculating physical properties dependent on connectivity incurs large uncertainties. Recording high-resolution tomograms of sections of the larger core plug may resolve this problem by fully resolving the pore-space, allowing direct calculations of transport properties. Mapping these high-resolution calculations back to the low-resolution tomogram, and ultimately calculating physical properties for the large FoV covered by the low-resolution tomogram, requires accurate and efficient three-dimensional registration, classification and upscaling techniques. Knackstedt et al. (2013) integrated nano-scale SEM images with subplug micro-CT images of tight carbonate to recover the structure of intermediate intensity voxels and analyze the hydrocarbon distribution therein. Khalili et al. ( 2012) integrated X-ray micro-CT images of different resolution by establishing physical cross-correlations at high resolution utilizing kriging techniques and sequential Gaussian simulation. High-resolution information was carried over to the low-resolution tomogram by kriging with external drift, predicting permeability with uncertainty at the core plug scale with good accuracy. Both of these works considered carbonates where a hierarchy of length scales was difficult to establish. Correlations