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We study the impact of reactive flow on a complex carbonate rock whose pore space was resolved by X‐ray μ‐CT. A small core plug was imaged in the original and chemically altered states. The morphology of the resolved pore space was quantified using various approaches, such as sample‐spanning cluster analysis, capillary drainage transform (CDT), and Minkowski functionals. In particular, we carefully evaluated the changes in macropore connectivity by calculating the fraction of percolated cluster, the Euler characteristic of macroporous phase, and the critical pore diameter in pre‐ and post‐alteration states. We observed that the reactive flow mainly affected larger pores in the macropore region of the sample. More importantly, a huge increase in macropore connectivity, as indicated majorly by the CDT measurements and the Euler characteristic, was observed such that the poorly connected original macropore phase was converted to well‐connected ones after the reactive flow. In addition, the increases in the macropore surface area and integral mean curvature indicated that the oomoldic and interooidal pores of the rock were dominantly enlarged by the reactive fluid. Based on direct numerical simulations on the main flow paths (i.e., the macro‐connected pore phase), strong correlations were found between changes in transport properties and the evolution of connectivity and length scales of the macropore phase, such that simple power‐law models could be used to predict with acceptable accuracy. This suggests that the evolution of transport properties of the rock was mainly controlled by the changes in the macro‐connected porosity and the critical pore diameter. Plain Language Summary: X‐ray micro‐computed tomography has attracted significant attention over the recent two decades due to its outstanding capabilities to capture the evolution of 3D structures of porous media during various processes. This study visualizes and quantifies the reactive flow‐induced changes on the images of a complex carbonate rock. To this end, we acquired images of the rock sample at the original and chemically altered states. Standard methods were used to assess the evolution of the morphology of the resolved pore space. We observed that the reactive flow mainly affected larger pores of the rock and significantly increased pore connectivity. The subsequent numerical morphology simulations revealed that the oomoldic and interooidal pores of the rock were dominantly enlarged by the reactive fluid. Based on numerical property calculations, we found that the changes in the connectivity parameters mainly controlled the evolution of transport properties of the rock. Key Points: The μ‐CT‐based connectivity and capillary drainage transform analyses indicated significant changes in macropore connectivity of a carbonate due to reactive flowThe most changes in the critical pore size, Minkowski parameters, and the correlation length were found near the inlet of the sampleThe evolution of transport properties was mainly controlled by the changes in the macro‐connected porosity and the critical pore diameter [ABSTRACT FROM AUTHOR]

The detailed geological fine grids are upscaled to create reliably sized simulation coarse models to solve flow equations in a more efficient way. Any upscaling process results in a loss of accuracy, along with an increase of errors. Numerical dispersion, heterogeneity loss, and connectivity misrepresentation are responsible for the upscaling errors. Recognizing the source of each error, and the behavior of influential factors through upscaling process could provide an optimum level of upscaling and an evaluation of upscaling methods' accuracy. Despite the importance of upscaling error, little attention has been paid to this subject. This paper represents a rigorous analysis of the heterogeneity loss behavior associated with the relative permeability contrast and the mobility ratio under a waterflooding process. For this purpose, heterogeneous fine grid models are constructed by the fractional Brownian motion process. The models are upscaled by three upscaling factors. The models achieved are implemented to eliminate the impact of numerical error among upscaling errors in order to focus strictly on heterogeneity loss. Water-oil displacement simulation is then performed on fine and corresponding refined upscaled models at three different ratios of relative permeabilities and mobility ratios. In the next stage, the relation between flow performance error and heterogeneity loss is investigated by the heterogeneity loss plot. The slope of this plot provides the reservoir engineer an insight to evaluate the performance of upscaling methods and the behavior of the influential factors on upscaling errors. Moreover, by using the heterogeneity loss plot for each ratio, a limit of coarsening is presented. Based on the results, the heterogeneity loss error is affected more by the mobility ratio contrast than the relative permeability difference. Also, it is demonstrated that water-wet reservoirs with light oil are more sensitive to the level of upscaling. [ABSTRACT FROM AUTHOR]

Excessive water production from hydrocarbon reservoirs is considered as one of major problems, which has numerous economic and environmental consequences. Polymer-gel remediation has been widely used to reduce excessive water production during oil and gas recovery by plugging high permeability zones and improving conformance control. In this paper, we investigate the performance of a HPAM/PEI (water-soluble Hydrolyzed PolyAcrylaMide/PolyEthyleneImine) polymer-gel system for pore space blockage and permeability reduction for conformance control purpose. First, the gel optimum composition, resistance to salt and long life time are determined using bottle tests as a standard method to specify polymer-gel properties. Then the performance and stability of the optimized polymer-gel are tested experimentally using coreflood tests in sandpack core samples. The effects of different parameters such as gel concentration, initial permeability of the cores, and formation water salinity on the final permeability of the cores are examined. Finally, the gel flowinduced local porosity changes are studied in both a sandpack core and a real carbonate sample using grayscale intensity data provided from 3D Computed Tomography (CT) images in pre- and post-treatment states. The results show that the gel system has a good strength at the middle formation water salinity (in the range of typical sea water salinity). In addition, despite a higher performance in high permeability cores, the gel resistance to degradation in such porous media is reduced. The CT images reveal that the initial porosity distribution has a great influence on the performance of the gel to block the pore space. [ABSTRACT FROM AUTHOR]

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]

• 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]