Carbonate formations consist of a wide range of pore types with different shapes, pore-throat sizes, and varying levels of pore-network connectivity. Such heterogeneous pore-network properties affect the fluid flow in the formation. However, characterizing pore-network properties (e.g., effective porosity and permeability) in carbonate formations is challenging due to the heterogeneity at different scales and complex pore structure of carbonate rocks. We have developed an integrated technique for multiscale characterization of carbonate pore structure based on mercury injection capillary pressure (MICP) measurements, X-ray micro-computed tomography (micro-CT) 3D rock images, and well logs. We have determined pore types based on the pore-throat radius distributions obtained from MICP measurements. We developed a new method for improved assessment of effective porosity and permeability in the well-log domain using pore-scale numerical simulations of fluid flow and electric current flow in 3D micro-CT core images obtained in each pore type. Finally, we conducted petrophysical rock classification based on the depth-by-depth estimates of effective porosity, permeability, volumetric concentrations of minerals, and pore types using an unsupervised artificial neural network. We have successfully applied the proposed technique to three wells in the Scurry Area Canyon Reef Operators Committee (SACROC ) Unit. Our results find that electrical resistivity measurements can be used for reliable characterization of pore structure and assessment of effective porosity and permeability in carbonate formations. The estimates of permeability in the well-log domain were cross-validated using the available core measurements. We have observed a 34% improvement in relative errors in well-log-based estimates of permeability, as compared with the core-based porosity-permeability models.