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Percolation of reactive fluids in carbonate rocks affects the rock microstructure and hence changes the rock macroscopic properties. In Part 1 paper, we examined the voxel-wise evolution of microstructure of the rock in terms of mineral dissolution/detachment, mineral deposition, and unchanged regions. In the present work, we investigate the relationships between changes in two characteristic transport properties, i.e. permeability and electrical conductivity and two critical parameters of the pore phase, i.e. the fraction of the pore space connecting the inlet and outlet faces of the core sample and the critical pore-throat diameter. We calculate the aforementioned properties on the images of the sample, wherein a homogeneous modification of pore structure occurred in order to ensure the representativeness of the calculated transport properties at the core scale. From images, the evolution of pore connectivity and the potential role of micropores on the connectivity are quantified. It is found that the changing permeability and electrical conductivity distributions along the core length are generally in good agreement with the longitudinal evolution of macro-connected macroporosity and the critical pore-throat diameter. We incorporate microporosity into critical length and permeability calculations and show how microporosity locally plays a role in permeability. It is shown that the Katz-Thompson model reasonably predicts the post-alteration permeability in terms of pre-alteration simulated parameters. This suggests that the evolution of permeability and electrical conductivity of the studied complex carbonate core are controlled by the changes in the macro-connected macroporosity as well as the smallest pore-throats between the connected macropores. [ABSTRACT FROM AUTHOR]

The application of X-ray micro-computed tomography (μ-CT) for quantitatively characterizing reactive-flow induced pore structure evolution including local particle detachment, displacement and deposition in carbonate rocks is investigated. In the studies conducted in this field of research, the experimental procedure has involved alternating steps of imaging and ex-situ core sample alteration. Practically, it is impossible to return the sample, with micron precision, to the same position and orientation. Furthermore, successive images of a sample in pre- and post-alteration states are usually taken at different conditions such as different scales, resolutions and signal-to-noise ratios. These conditions accompanying with subresolution features in the images make voxel-by-voxel comparisons of successive images problematic. In this paper, we first address the respective challenges in voxel-wise interpretation of successive images of carbonate rocks subject to reactive flow. Reactive coreflood in two carbonate cores with different rock types are considered. For the first rock, we used the experimental and imaging results published by Qajar et al. (2013) which showed a quasi-uniform dissolution regime. A similar reactive core flood was conducted in the second rock which resulted in wormhole-like dissolution regime. We particularly examine the major image processing operations such as transformation of images to the same grey-scale, noise filtering and segmentation thresholding and propose quantitative methods to evaluate the effectiveness of these operations in voxel-wise analysis of successive images of a sample. In the second part, we generalize the methodology based on the three-phase segmentation of normalized images, microporosity assignment and 2D histogram of image intensities to estimate grey-scale changes of individual image voxels for a general case where the greyscale images are segmented into arbitrary number of phases. The results show that local (voxel-based) porosity changes can be decomposed into local mineral dissolution and deposition. Moreover, it is found that the microporosity evolutions are differently distributed in the samples after the reactive coreflood experiments. In the last part of the paper, for the case of quasi-uniform dissolution, we combine the tomographic images with numerical calculations of permeability along the core to characterize the relationship between changes in permeability and the fractions of the mineral dissolved and deposited. A consistency is found between the calculated longitudinal permeability changes and the quantified distribution of mineral dissolved and deposited along the sample. [ABSTRACT FROM AUTHOR]

• Effects of chemical dissolution on pore space was explored by μ-CT and MIP methods. • The evolution of pore size was examined by experimental and simulated MIP curves. • Impact of micropores on pore surface area was quantified by combining μ-CT and MIP. • Permeability changes were controlled by macroporosity and critical pore diameter. In this paper, we report on experimental and computational studies investigating the evolution of pore structure and permeability of a microporous carbonate rock during chemical dissolution using information provided from X-ray micro-computed tomography (µ-CT) and mercury intrusion porosimetry (MIP) techniques. We consider a chemical dissolution in a core sample by a nonacidic solution wherein a quasi-uniform modification of pore structure occurred. First, we conduct a comparative analysis on the capabilities and limitations of the µ-CT and MIP methods to quantify the evolution of pore size and pore surface area. In particular, we incorporate micropores into the calculations of the pore size-related parameters to highlight the uncertainties that ignoring microporosity causes. In the second part of the paper, we present predictive results on the fractional changes in the permeability of the rock using the Katz-Thompson (KT) and Kozeny-Carman (KC) models. The predictions are based on two characteristic parameters of the pore phase, i.e., the critical pore diameter and the specific pore surface area, and two characteristic macro-scale properties, i.e., porosity and formation factor, all calculated from both the MIP and µ-CT methods. The predicted changes in permeability are compared with those calculated directly on the images using the lattice-Boltzmann (LB) flow simulation on segmented images. The effect of changes in microporosity is also assessed to investigate its role in permeability evolution. The results showed that the KT model could reasonably predict the fractional changes in permeability in terms of the pore structure and macroscale parameters calculated from the MIP and µ-CT methods. [ABSTRACT FROM AUTHOR]

This study aims to present a simple method to calculate the permeability of porous materials using μ-CT images and an approximation based on the solution of Laplace's equation for pressure. For this purpose, an in-house computer program was developed based on finite difference method to determine the distribution of pressure in voxelated pore space. Afterwards, approximate permeability was obtained using Euclidean distance map of the pore space and a simple upscaling scheme for a range of porous media including idealized channels of elementary cross sections, Boolean models of spherical grains, bundles of capillary tubes and digital rocks obtained from μ-CT imaging. Next, a comparison was made between the Laplace permeability approximation and the analytical solutions of idealized microstructure and digitally-computed permeability of a number of rock samples using the Stokes solver and the lattice-Boltzmann method. It has been revealed that the estimated permeability values are in good agreement over the investigated range of porosity. The developed Laplace permeability solver was also well matched with the results of experimental measurement on a real rock sample. As an illustration of the applicability of the proposed method, the anisotropy of permeability at pore scale and the cross-correlation between permeability and connected macro-porosity was analyzed for the experimentally investigated rock sample. In conclusion, the results of this paper suggest that the proposed permeability solver is suitable for rough estimation of permeability and hence rough evaluation of heterogeneity/anisotropy from μ-CT images of rocks, particularly for large datasets with high number of pore voxels. • A simple method for estimating permeability of rocks using 3D µ-CT images. • The method is based on a finite-difference solution of Laplace's equation for pressure. • An upscaling scheme is used to estimate permeability using the pressure and Euclidean distance maps of the fluid phase. • Highlights The method is compared with analytical solutions of idealized media and Stokes and LBM calculations of digital rocks. [ABSTRACT FROM AUTHOR]

Digital rock physics is at the forefront of characterizing porous media, leveraging advanced tomographic imaging and numerical simulations to extract key rock properties like permeability. However, fully capturing the heterogeneity of natural rocks necessitates imaging increasingly larger sample volumes, presenting a significant challenge. Direct numerical simulations at these scales become either prohibitively expensive or computationally unfeasible due to limitations in resolution and field of view (FOV). This issue is particularly pronounced in carbonate rocks, known for their complex, multiscale pore structures, which exacerbate the resolution-FOV tradeoff. To address this, we introduce a machine learning strategy that merges multiscale imaging data from various resolutions with a 3D convolutional neural network (CNN) model. This approach is innovative in its ability to identify cross-scale correlations, thereby enabling the estimation of transport properties in larger volumes—properties that are difficult to simulate directly—using trainable proxies. The integration of multiscale imaging with deep learning allows for accurate permeability predictions at scales beyond those feasible with traditional direct simulation methods. By employing transfer learning across different scales during the training phase, our multiscale machine learning model achieves robust performance, with an R² exceeding 0.96 when evaluated on diverse lower-resolution domains with larger FOVs. Notably, this method significantly enhances computational efficiency, reducing the computational time by orders of magnitude. Originally developed for the intricate pore structures of carbonate rocks, our approach shows promise for application to a wide range of multiscale porous media, offering a viable solution to the longstanding tradeoff between imaging resolution and FOV in digital rock physics. [ABSTRACT FROM AUTHOR]