Fine-grained sandstones, siltstones, and shales have become increasingly important to satisfy the ever-growing global energy demands. Of particular current interest are shale rocks,
which are mudstones made up of organic and inorganic constituents of varying pore sizes.
These materials exhibit high heterogeneity, low porosity, varying chemical composition
and low pore connectivity. Due to the complexity and the importance of such materials,
many experimental, theoretical and computational eforts have attempted to quantify the
impact of rock features on fuids difusivity and ultimately on permeability. In this study,
we introduce a stochastic kinetic Monte Carlo approach developed to simulate fuid transport. The features of this approach allow us to discuss the applicability of 2D vs 3D models for the calculation of transport properties. It is found that a successful model should
consider realistic 3D pore networks consisting of pore bodies that communicate via pore
throats, which however requires a prohibitive amount of computational resources. To overcome current limitations, we present a rigorous protocol to stochastically generate synthetic 3D pore networks in which pore features can be isolated and varied systematically
and individually. These synthetic networks do not correspond to real sample scenarios but
are crucial to achieve a systematic evaluation of the pore features on the transport properties. Using this protocol, we quantify the contribution of the pore network’s connectivity,
porosity, mineralogy, and pore throat width distribution on the difusivity of supercritical
methane. A sensitivity analysis is conducted to rank the signifcance of the various network
features on methane difusivity. Connectivity is found to be the most important descriptor,
followed by pore throat width distribution and porosity. Based on such insights, recommendations are provided on possible technological approaches to enhance fuid transport
through shale rocks and equally complex pore networks. The purpose of this work is to
identify the signifcance of various pore network characteristics using a stochastic KMC
algorithm to simulate the transport of fuids. Our fndings could be relevant for applications
that make use of porous media, ranging from catalysis to radioactive waste management,
and from environmental remediation to shale gas production
