Imaging techniques such as X-ray microtomography allow us to visualize how pore space is arranged in a material. The quantitative analysis of these images gives detailed information about the basic textural properties, distribution, arrangement, and connectivity of the pores. For instance, [PERSON] et al. [7] provided the investigation of shale pore structure characteristics by applying field emission or focused ion beam scanning electron microscopy (FE-/FIB-SEM). The integration of this microstructure data with other techniques is also beneficial for assessing the pore structure influence on flow properties. Combining computer tomography (CT) scanning and microscopic seepage simulation allowed authors to evaluate quantitatively the effectiveness of fractures [8]. [PERSON] et al. [9] performed flow simulation on digital rock models obtained from CT data to investigate the effect of the adsorption boundary layer on the fluid flow. [PERSON] and [PERSON] [10] explored the permeability of shale by simulating pulse-decay experiments on a discrete fracture model based on 3D scanner results. [PERSON] et al. [11] proposed a methodology for estimating the pore connectivity in carbonate granular media using the number and size of the backbones (parts of the connected pore network) and the \"cluster multiple labeling\" techniques according to which connectivity is a ratio of the volume of the largest backbones of pores to the volume of all pores in the sample. For carbonate grainstones, [PERSON] et al. [12] provided an assessment of pore connectivity through the following parameters--connected porosity, specific surface, and connectivity density. [PERSON] et al. [13] considered connectivity measures such as the connection probability, the Euler density, and the critical pore diameter for soil samples. Due to the limited resolution of the imaging technique and ambiguity in the identification of individual phases (segmentation problem) and in the definition of representative elementary volume, these quantitative characteristics may be difficult to identify in rocks containing microporosity, hence these connectivity measures are not reliable enough. Owing to real porous space complexity for analyses of pore network morphologies, researchers usually apply some simplifications representing pore space as morphological (pore-throat) networks, consisting of spherical pores and cylindrical throats, or random networks. In this case, coordination number or mean coordination number and radius of the pore provide information about the pore connectivity [14; 15; 16; 17]. The coordination number is the number of throats (channels) connected to a nodal pore, but connectivity metrics from percolation theory are also widely used. They are the percolation probability [18; 19], the connected porosity [20], and the percolation threshold [19; 21]. [PERSON] [22] uses [PERSON]'s laws to propose a measure of connectivity. He defines connectedness as the inverse of the formation resistivity factor and shows that connectedness is the product of porosity and connectivity. The connectivity itself depends upon the porosity and the cementation exponent from [PERSON]'s law. [PERSON] [23] associates the connectivity of a random network with the conductivity equations obtained from percolation and effective medium theory [24]. The author presents the connectivity equation with two parameters--a conductivity exponent and a water connectivity index. The combination of this equation with modified mixing law allows to account for variations of water connectivity within the rocks. [PERSON] [23] successfully applied his theory to obtain analytical conductivity models of shale sands, oil-wet rocks, and mixed-wet microlitric carbonates. [PERSON] et al. [25] considered a groundwater flow through an aquifer and adopted the hydraulic response time between two points in a pumping test as a flow connectivity indicator. Since permeability is a scale-dependent property, the main factor controlling the pore connectivity also should be considered to be dependent on the scale.