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.

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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 (μ-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 non-acidic solution is designed such that changes in pore space from before to after the reactant injection could be imaged in exactly the same locations using μ-CT at resolution of less than 5μm. A methodology based on three-phase segmentation and 2D intensity histograms of images 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 in order to accurately quantify the evolution scenarios. The µ-CT images reveal a quasi-uniform dissolution pattern and allow characterizing the accompanying migration of fines within the core sample. 3D pore networks are derived from the image data, which quantify changes in network structure and the pore geometry. 2D histograms of image intensity derived from the pre-and post-dissolution images quantitatively show how macro and micropores are enlarged by dissolution close to the inlet while 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 as injection time increases. Based on the spatially resolved voxel evolution scenarios, pore surface area between each region is computed. 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.

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Pressure transient data from downhole gauges are one of the key parameters in characterizing reservoir properties and forecasting future reservoir performance. Reservoir pressure is usually measured under dynamic changes. The collected data usually contain different levels of noise, particularly due to imperfections in measuring instruments and imperfect calibrations. The latter is due to changes between the laboratory environment and reservoir conditions. To have accurate descriptions of reservoir, it is essential to smooth the pressure data. Most related studies have employed the wavelet transform to reduce noise. However, there appears to be little research addressing the use of other smoothing techniques for pressure transient data. This paper, therefore, evaluates and compares the performance of three types of smoothing and noise removal methods, namely wavelet transform as a widely used filtering technique, regression-based smoothers, and autoregressive smoothing methods to reduce artificial noise added to simulated dual-porosity pressure data. Particularly, noise is more pronounced in pressure derivative, and so denoising of pressure derivative requires more effective tools. The effectiveness of the noise removing methods was compared using mean square error. The results show that the regression-based methods lead to the same or even better reduction in the noise level as compared to the wavelet domain filter, while the employed autoregressive method results in a moderate performance. We also test the performance of various combinations of the different smoothing methods to filter the same noisy data. It is shown that the combined locally weighted scatterplot smooth (LOESS) and autoregressive moving average (ARMA) gives the best smoothing performance for pressure derivative data. Application of the combined LOESS-ARMA to real field data shows promising results.