The shapes of particles play a crucial role in the transport and mechanical behavior of granular media. How densely particles of a specific shape can be arranged in a given volume has an enormous practical significance for many scientific and industrial fields. We developed a numerical workflow to investigate the effects of irregularly shaped frictionless, convex particles combined with the polydispersity of particle sizes on the porosity, permeability, and elastic bulk modulus of granular porous media. The workflow is based on the computationally generated three‐dimensional granular assemblies of both regular and irregularly shaped particles and pore‐scale simulation of physics to estimate the absolute permeability and elastic bulk modulus of the generated digital granular porous media. Our numerical results show that the porosity decreases by 14–25%, absolute permeability decreases by 45–76%, and elastic bulk modulus increases by 20–66% as we deviate from the regular‐shaped particle with the specified particle size distribution. How rapidly and to what degree these properties of granular assemblies of irregularly shaped particles change depends on particle size distribution. However, once the maximal densest packings of irregularly shaped particles with the prescribed aspect ratio are generated, any further change in the shape of particles has either a minimal impact or an opposite effects are observed. Overall, our numerical observations suggest that the irregular shape of particles has the greatest effect on permeability and the least effect on porosity in both monodisperse and polydisperse granular media.
Mechanical trapping of fine particles in the pores of granular materials is an essential mechanism in a wide variety of natural and industrial filtration processes. The progress of invading particles is primarily limited by the network of pore throats and connected pathways encountered by the particles during their motion through the porous medium. Trapping of invading particles is limited to a depth defined by the size, shape, and distribution of the invading particles with respect to the size, shape, and distribution of the host porous matrix. Therefore, the trapping process, in principle, can be used to obtain information about geometrical properties, such as pore throat and particle size, of the underlying host matrix. A numerical framework is developed to simulate the mechanical trapping of fine particles in porous granular media with prescribed host particle size, shape, and distribution. The trapping of invading particles is systematically modeled in host packings with different host particle distributions: monodisperse, bidisperse, and polydisperse distributions of host particle sizes. Our simulation results show quantitatively and qualitatively to what extent trapping behavior is different in the generated monodisperse, bidisperse, and polydisperse packings of spherical particles. Depending on host particle size and distribution, the information about extreme estimates of minimal pore throat sizes of the connected pathways in the underlying host matrix can be inferred from trapping features, such as the fraction of trapped particles as a function of invading particle size. The presence of connected pathways with minimum and maximum of minimal pore throat diameters can be directly obtained from trapping features. This limited information about the extreme estimates of pore throat sizes of the connected pathways in the host granular media inferred from our numerical simulations is consistent with simple geometrical estimates of extreme value of pore and throat sizes of the densest structural arrangements of spherical particles and geometrical Delaunay tessellation analysis of the pore space of host granular media. Our results suggest simple relations between the host particle size and trapping features. These relationships can be potentially used to describe both the dynamics of the mechanical trapping process and the geometrical properties of the host granular media.
Rock compressibility is a major control of reservoir compaction, yet only limited core measurements are available to constrain estimates. Improved analytical and computational estimates of rock compressibility of reservoir rock can improve forecasts of reservoir production performance and the geomechanical integrity of compacting reservoirs. The fast-evolving digital rock technology can potentially overcome the need for simplification of pores (e.g., ellipsoids) to estimate rock compressibility as the computations are performed on an actual pore-scale image acquired using 3D microcomputed tomography (micro-CT). However, the computed compressibility using a digital image is impacted by numerous factors, including imaging conditions, image segmentation, constituent properties, choice of numerical simulator, rock field of view, how well the grain contacts are resolved in an image, and the treatment of grain-to-grain contacts. We have analyzed these factors and quantify their relative contribution to the rock moduli computed using micro-CT images of six rocks: a Fontainebleau sandstone sample, two Berea sandstone samples, a Castelgate sandstone sample, a grain pack, and a reservoir rock. We find that image-computed rock moduli are considerably stiffer than those inferred using laboratory-measured ultrasonic velocities. This disagreement cannot be solely explained by any one of the many controls when considered in isolation, but it can be ranked by their relative contribution to the overall rock compressibility. Among these factors, the image resolution generally has the largest impact on the quality of image-derived compressibility. For elasticity simulations, the quality of an image resolution is controlled by the ratio of the contact length and image voxel size. Images of poor resolution overestimate contact lengths, resulting in stiffer simulation results.
Granular dynamics simulations provide insights to the contact-scale physics of loose sediments. However, simulations using identical spherical grains do not reflect characteristics observed in natural sediments, such as pack sorting, grading, grain sphericity, and grain roundness. We have developed software to create 3D grain packs of a range of regular and irregular shapes with geologically realistic variations in sorting and grading. An efficient approach is used to create multiple realizations of nonspherical irregularly shaped grains using coherent noise modification of the spherical grain surface. The discrete-element method is used to assemble the grain pack with different depositional styles by letting grains fall under the influence of gravity. Characterization of various parameters of random loose and dense grain packs, and comparison with previous studies, helps to establish the validity, flexibility, and consistency of the simulator. The output of this software is a digital grain pack, including metadata such as contacts and coordinates, that can be studied further using other analysis tools, e.g., by conducting fluid flow, mechanical, or electrical simulations.
A digital 3‐D representation of a heterogeneous medium can be used as an input to physics‐based numerical methods to obtain an estimate of the effective physical properties of the composite, such as transport, electrical, and elastic properties. These properties can then be used to simplify large‐scale numerical simulations of physical processes by considering the constitutive equations of the heterogeneous composite as if it was a homogenous continuum. By definition, effective physical properties do not depend on the boundary conditions applied to a medium during an experiment. In finite‐sized media, computing effective properties for a medium may not be feasible due to the boundary effects on the computed parameters. Through numerical experiments, we developed a methodology to minimize the boundary effects by considering a subdomain within a sample onto which the experiment was performed. Specifically, we analyzed the boundary effects on the computed elastic properties of 2‐D and 3‐D subdomains located within a digitized sample of a Berea sandstone and a compacted random pack of identical spherical particles. As the parent sample size increases while the size of the subdomain remains fixed, the elastic properties of the latter converge to constant values. These values correspond to the effective elastic values of the subsample, though they depend on the immediate region surrounding the subdomain. In addition, we propose a method to relate the effective elastic properties of rock samples to another microstructural parameter to be used in prediction of elastic properties of other samples having the same type and geological conditions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.