Understanding fluid flow and transport within clay rock is essential for predicting caprock integrity in underground gas storage or as host rock in deep radioactive waste storage. The connectivity and topology of the nanopore space, which drive the transfer mechanisms of these materials, are still poorly known and direct 3D imaging is particularly challenging. In this work, we investigate and compare different stochastic reconstruction approaches based on two-point and multiple-point statistics (MPS) methods and using information from 2D training images for 3D volume rendering at submicron scale. A particular emphasis is given to the maximal critical distance of sampling between two consecutive 2D images which is necessary to obtain a coherent 3D reconstruction of the nanopore structure. We assess how these realizations honour various crucial transport properties of material, namely permeability, effective diffusion and longitudinal dispersion. Morphological features such as pore volume, specific surface, Euler characteristic and tortuosity are used to analyze the results. The methods are employed on a synthetic clay of nanometric porosity for which FIB-SEM images are available. Results indicate that the 3DA-MPS and weighted-3DA-MPS approaches are the most suited for preserving pore space features and transport properties, the choice depending on the level of conditioning data available.