Abstract
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 efforts have attempted to quantify the impact of rock features on fluids diffusivity and ultimately on permeability. In this study, we introduce a stochastic kinetic Monte Carlo approach developed to simulate fluid 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 diffusivity of supercritical methane. A sensitivity analysis is conducted to rank the significance of the various network features on methane diffusivity. 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 fluid transport through shale rocks and equally complex pore networks. The purpose of this work is to identify the significance of various pore network characteristics using a stochastic KMC algorithm to simulate the transport of fluids. Our findings 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.
Highlights
In view of the growing demand for energy, production rocks that were once considered non-reservoirs, such as fine-grained sandstones, siltstones and shales, are becoming increasingly important
It is known that fluid flow and mass transport through rocks are controlled by pore network characteristics such as pore size distribution, pore connectivity, porosity, pore throat width distribution and mineralogy (Bear 1972; Dullien 1992; Chalmers et al 2012; Kwon et al 2004)
When a pore is connected to other pores, the slit pore throats used as channels have different widths
Summary
In view of the growing demand for energy, production rocks that were once considered non-reservoirs, such as fine-grained sandstones, siltstones and shales, are becoming increasingly important. Sahimi described pore connectivity by an equivalent network of pore throats, i.e., narrow passages through which fluids flow, and pore bodies, large voids that meet through the throats (Sahimi 2011). This feature of the porous media, frequently referred to as topology, pore throat connectivity or coordination number, has been widely identified as one of the most important parameters that affect transport in many porous media, including shale rocks (Chen et al 2003; Vasilyev et al 2012). Backward methods first quantify some macroscopic characteristics of the porous media, e.g., capillary pressure, porosity or relative permeability, and back-calculate the features of the underlying pore network model (Mata et al 2001; Tsakiroglou and Payatakes 2000; Davudov et al 2019; King et al 2015; Chalmers et al 2012)
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