Summary: The microstructure of carbonate rocks experiences substantial changes under reactive processes, in particular chemical dissolution and deposition, including dissolution-released-fines migration occurring during acidizing. A better understanding of such changes at the pore scale and their influences on rock properties is of great value for the effective design and implementation of reactive processes in carbonate reservoirs. In this work, we demonstrate the use of X-ray micro-computed tomography (micro-CT) to quantitatively investigate the local porosity changes in a meso-/microporous carbonate core sample during chemical dissolution. A reactive flooding experiment in a core sample by a nonacidic solution is designed such that changes in pore space from before to after the reactant injection could be imaged in exactly the same locations with micro-CT at a resolution of less than 5 μm. A methodology with three-phase segmentation and 2D histograms of image intensity is used to quantify distributions of the evolution of each image voxel. This technique allows the incorporation of microporosity into the calculation of the evolution regions, including the migration of fines, to accurately quantify the evolution scenarios. The micro-CT images reveal a quasiuniform dissolution pattern and allow characterizing the accompanying migration of fines within the core sample. The 3D pore networks are derived from the image data, which quantify changes in network structure and the pore geometry. The 2D histograms of image intensity derived from the pre- and post-dissolution images show quantitatively how macro- and micropores are enlarged by dissolution close to the inlet, whereas the deposition of fines mainly occurs in pores far from the inlet boundary. These results can explain why permeability of the sample initially decreases and then increases when injection time increases. Pore-surface area between each region is computed on the basis of the spatially resolved voxel evolution scenarios. This allows calculation of local distribution of reactive surface area, which, in turn, will assist in the prediction of local reaction rates in reactive flow simulators.

Accurately determining rock elastic modulus (EM) and uniaxial compressive strength (UCS) using laboratory methods requires considerable time and cost. Hence, the development of models for estimating the mechanical properties of rock is a very attractive alternative. The current research was conducted to predict the UCS and EM of sandstone rocks using quartz%, feldspar%, fragments%, compressional wave velocity (PW), the Schmidt hardness number (SN), porosity, density, and water absorption via simple regression, multivariate regression (MVR), K-nearest neighbor (KNN), support vector regression (SVR) with a radial basis function, the adaptive neuro-fuzzy inference system (ANFIS) using the Gaussian membership (GM) function, and the back-propagation neural network (BPNN) based on various training algorithms. The samples were categorized as litharenite and feldspathic litharenite. By increasing the feldspar% and quartz% and decreasing the fragments%, the static properties increased. The results of the statistical analysis showed that the SN and porosity have the greatest effect on the UCS and EM, respectively. Among the Levenberg–Marquardt (LM), Bayesian regularization, and Scaled Conjugate Gradient training algorithms using the BPNN method, the LM achieved the best results in forecasting the UCS and EM. The ideal obtained BPNN, using a trial-and-error process, contains four neurons in a hidden layer with eight inputs. All five models attained acceptable accuracy (correlation coefficient greater than 70%) for estimating the static properties. By comparing the methods, the ANFIS showed higher precision than the other methods. The UCS and EM of the samples can be determined with very high accuracy (R² > 99%).