Image‐based modeling of granular porous media

Tahmasebi, Pejman; Sahimi, Muhammad; Andrade, José E.

doi:10.1002/2017gl073938

Cited by 62 publications

(26 citation statements)

References 78 publications

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“…; Karimpouli et al . , ; Karimpouli, Tahmasebi and Saenger ; Tahmasebi, Sahimi and Andrade ; Tahmasebi ,b; Tahmasebi ,b,c). Therefore, six real 3D digital samples, three sandstones (Bentheimer, Clashach and Doddington) and three carbonates (Estaillades, Ketton and Portland), are used in this study.…”

Section: Introductionmentioning

confidence: 99%

3D multi‐fractal analysis of porous media using 3D digital images: considerations for heterogeneity evaluation

Karimpouli

Tahmasebi

2018

Geophysical Prospecting

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Pore structure heterogeneity is a critical parameter controlling mechanical, electrical and flow transport behaviour of rock. Multi‐fractal analysis method was used for a heterogeneity comparison of three‐dimensional rock samples with different lithology. Six real digital samples, containing three sandstones and three carbonates, were used. Based on the mercury injection capillary pressure test on these samples, we found that the carbonate samples are more heterogeneous than sandstones, but primary results of multi‐fractal behaviours for all samples were similar. We show that if multi‐fractal is used to evaluate and compare heterogeneity of different samples, one needs to follow some considerations such as (1) all samples must have the same size in pixel, (2) samples volume must be bigger than representative volume element, (3) multi‐fractal dimensions should be firstly normalized to a determined porosity value and (4) multi‐fractal results should be interpreted based on resolution of the imaging tool (effects of fine scale sub‐resolution pores are missed). Results revealed that using normalized fractal dimensions, the real samples were divided to less and high heterogeneous groups. Moreover, the study of scale effect also showed that porous structures of these samples are scale invariant in a wide range of scales (from one to eight times bigger).

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Section: Introductionmentioning

confidence: 99%

3D multi‐fractal analysis of porous media using 3D digital images: considerations for heterogeneity evaluation

Karimpouli

Tahmasebi

2018

Geophysical Prospecting

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“…Vast experimental data (Sahimi, ) indicate that natural grains are not smooth; rather, they possess rough surface, with the roughness described accurately by a self‐affine fractal profile. There are many methods by which one can take into account the rough surface of the grains and their irregular shapes (Tahmasebi & Sahimi, ; Tahmasebi et al, ). In particular, Askari, Hejazi, and Sahimi () developed a new model of granular media with two important features: (i) The grains' surface was rough and had the structure of a self‐affine fractal and (ii) the effect of elastic deformation due to a compressive pressure as a function of the Young's modulus Y of the grain was incorporated through a fractal contact model (see below).…”

Section: Models and Simulation Methodsmentioning

confidence: 99%

Effect of Elastic Deformation and Rough Grain Surface on Heat Conduction in Partially Saturated Granular Porous Media

Ikram

Askari

Hejazi

et al. 2018

Water Resources Research

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Accurate prediction of the effective thermal properties of partially saturated rock, soil, and other types of porous media is essential to a wide variety of phenomena and processes in engineering and natural sciences. The effective thermal conductivity of porous media depends strongly on the morphology of the media and the fluid distribution in the pore space, as well as on geomechanical processes such as dilation and microcracking. A precursor to such processes is the deformation of the grains' surface due to the overburden pressure that is dependent on pore‐scale features, such as the surface texture of the grains. We report on the results of the extensive two‐dimensional computer simulations of heat conduction of granular porous media in which the grains have rough, self‐affine fractal surface, and water partially saturates the pore space, and the media are subjected to an external compressive pressure. The Young's modulus of the grains and the fractal dimension of their surface profile dictate the contact area for the deformation. The spatial distribution of the fluids and the resulting saturation is generated by a lattice Boltzmann method, while the conduction process is simulated by a thermal lattice Boltzmann. Two essentially linear dependence of the effective thermal conductivity on the water saturation emerge, separated at a critical water saturation Sc that signals the formation of percolation clusters of water‐filled pores. One interval, below Sc, is dominated by heat conduction through the solid phase, assisted by conducting water globules that bridge the noncontacting grains. The second interval emerges above Sc in which the water‐filled pores create a percolating conductive cluster that dominates heat conduction through the medium. The effect of various factors on the conduction process is studied in detail.

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“…Most methods reported in Table 3 incur a high computational cost per generated realization, with the exception of the method of Tahmasebi et al (2017). We refer to these methods as proportional cost methods as the computational cost scales linearly with the number of created realizations.…”

Section: Discussionmentioning

confidence: 99%

“…Each column represents one layer of the discriminator network. Each row represents one learned filter kernel in each layer applied to the input (leftmost column) 2011; Tahmasebi et al 2017). While considering the resulting image quality to be equal, one possible differentiation of these methods is computational run time.…”

Section: Discussionmentioning

confidence: 99%

“…This approach is similar to the image quilting algorithm by Efros and Freeman (2001) and Mariethoz and Caers (2014) but corrects mismatching patches in overlapping or neighboring domains. Tahmasebi et al (2017) present a method for fast reconstruction of granular porous media from a single twoor three-dimensional training image using a method closely related to CCSIM. They obtain significant speedup in computational time by incorporating a fast Fourier transform and a multi-scale approach.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic Reconstruction of an Oolitic Limestone by Generative Adversarial Networks

2018

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Stochastic image reconstruction is a key part of modern digital rock physics and material analysis that aims to create representative samples of microstructures for upsampling, upscaling and uncertainty quantification. We present new results of a method of three-dimensional stochastic image reconstruction based on generative adversarial neural networks (GANs). GANs are a family of unsupervised learning methods that require no a priori inference of the probability distribution associated with the training data. Thanks to the use of two convolutional neural networks, the discriminator and the generator, in the training phase, and only the generator in the simulation phase, GANs allow the sampling of large and realistic volumetric images. We apply a GAN-based workflow of training and image generation to an oolitic Ketton limestone micro-CT unsegmented gray-level dataset. Minkowski functionals calculated as a function of the segmentation threshold are compared between simulated and acquired images. Flow simulations are run on the segmented images, and effective permeability and velocity distributions of simulated flow are also compared. Results show that GANs allow a fast and accurate reconstruction of the evaluated image dataset. We discuss the performance of GANs in relation to other simulation techniques and stress the benefits resulting from the use of convolutional neural networks . We address a number of challenges involved in GANs, in particular the representation of the probability distribution associated with the training data.

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Image‐based modeling of granular porous media

Cited by 62 publications

References 78 publications

3D multi‐fractal analysis of porous media using 3D digital images: considerations for heterogeneity evaluation

3D multi‐fractal analysis of porous media using 3D digital images: considerations for heterogeneity evaluation

Effect of Elastic Deformation and Rough Grain Surface on Heat Conduction in Partially Saturated Granular Porous Media

Stochastic Reconstruction of an Oolitic Limestone by Generative Adversarial Networks

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