The Impact of Pore Structure Heterogeneity, Transport, and Reaction Conditions on Fluid–Fluid Reaction Rate Studied on Images of Pore Space

Alhashmi, Z.; Blunt, Martin J.; Bijeljic, Branko

doi:10.1007/s11242-016-0758-z

Cited by 42 publications

(42 citation statements)

References 34 publications

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“…), CO 2 transport (Oh et al . ), fluid–fluid reaction rate (Alhashmi, Blunt and Bijeljic ), elastic wave propagation (Hamzehpour, Asgari and Sahimi ) and electrical properties (Xin et al . ).…”

Section: Introductionmentioning

confidence: 99%

“…Heterogeneity assessment is considered as a critical study in a wide range of applications in rock physics, especially in petroleum industry and hydrogeology. Some of its applications are on controlling fluid flow and reservoir production (Sahimi and Yortsos 1990;Rhodes, Bijeljic and Blunt 2008;Edery et al 2014), CO 2 transport (Oh et al 2017), fluid-fluid reaction rate (Alhashmi, Blunt and Bijeljic 2016), elastic wave propagation * E-mail: s.karimpouli@znu.ac.ir (Hamzehpour, Asgari and Sahimi 2016) and electrical properties (Xin et al 2016). Among all methods, fractal geometry (Mandelbrot 1982) has been widely utilized to characterize the heterogeneity of rock for more than three decades (Katz and Thompson 1985;Hansen and Skjeltorp 1988;Shen, Li and Jia 1995;Li and Horne 2003;Sahimi 2011;Al-Khidir et al 2013;Vega and Jouini 2015;Chattopadhyay and Vedanti 2016).…”

Section: Introductionmentioning

confidence: 99%

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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%

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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“…The velocity distributions of the numerical simulations performed on the Ketton validation dataset and generated realizations were normalized by the average cell-centered velocity following the approach of Alhashmi et al (2016) and a histogram with 256 logarithmically spaced bins in a range from 10 −4 to 10 2 for each simulation was obtained. Figure 11 shows the per-bin arithmetic average of the bin frequencies and a bounding region of one standard deviation μ ± σ as the shaded area.…”

Section: Permeability and Velocity Distributionsmentioning

confidence: 99%

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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“…The transport regime we work with is characterized by the Péclet number

P e = \frac{u_{av} \cdot L}{D_{m}}

with characteristic length L = 200.18·10 −6 m given by the grain size (Alhashmi et al, ; Mostaghimi et al, ). For the reference transport simulation at Pe = 100, we set the volume averaged velocity to

u_{av} = 1.09896 \cdot 1 0^{- 3} \frac{m}{s}

.…”

Section: Obtaining the Training Trajectories By Direct Numerical Simumentioning

confidence: 99%

Trajectories as Training Images to Simulate Advective‐Diffusive, Non‐Fickian Transport

Most

Bolster

Bijeljic

et al. 2019

Water Resources Research

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We propose a spatial Markov model to simulate transport in three‐dimensional complex porous media flows. Our methodology is inspired by the concept of training images from geostatistics. Instead of using a training image we use highly resolved training trajectories obtained by high‐resolution particle tracking, from which we sample increments in our random walk model. To reflect higher‐order processes, subsequent increments are correlated. The approach can be split into three steps. First, we subdivide (cut) the training trajectories to form an archive of trajectory segments. Next, we recursively sample segments, where subsequent samples are chosen conditioned to the previous one to ensure continuity and smoothness of velocity (conditional copy). Finally, we merge (paste) consecutive segments together to generate simulated trajectories of arbitrary length. This training trajectory approach aims to overcome three common shortcomings of spatial Markov models: (1) We simulate finite‐Péclet transport in three dimensions without commonly made simplifications (e.g., dimensionality reduction, and neglecting diffusion). (2) We do not parameterize dependence via a high‐dimensional transition matrix. (3) We simulate transport at the resolution of the (highly resolved) training trajectories, which can be important for processes such as mixing and reaction. To validate our methodology, we apply it to simulate transport within a three‐dimensional sandstone sample and compare predictions of a broad range of benchmark metrics against measurements from direct numerical simulations. We demonstrate that the training trajectories approach accurately represents three‐dimensional particle motion, suggesting that this method can capture the governing dependence structure and simulate transport processes in full complexity.

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The Impact of Pore Structure Heterogeneity, Transport, and Reaction Conditions on Fluid–Fluid Reaction Rate Studied on Images of Pore Space

Cited by 42 publications

References 34 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

Stochastic Reconstruction of an Oolitic Limestone by Generative Adversarial Networks

Trajectories as Training Images to Simulate Advective‐Diffusive, Non‐Fickian Transport

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