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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]

• 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]

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]