New Insights into Carbonate Matrix Acidizing Treatments: A Mathematical and Experimental Study

Abstract

Summary The design process of carbonate matrix acidizing treatments requires coring and conducting linear, radial coreflood experiments. With the current environment revolving around cutting costs, it has become increasingly important to accurately design cost-effective acidizing treatments. This work aims to introduce a novel approach to predicting the performance of acid treatments in the field using log data only. A radial reactive flow simulator, using porosity distributed from logs, is used to provide accurate predictions without the need for experiments. Coreflood acidizing experiments at 150 and 200°F with two acid concentrations were studied. A reactive flow simulator was built using porosity distribution derived from computed-tomography (CT) scans and tuned to match experimental data. A new radial simulation model of 3.25-ft radius was used to study acid propagation under field conditions. For accurate predictions, porosity was distributed using values derived from cores’ CT scans. Simulation results were compared with traditional 1D models. Different porosity distributions, including gamma distributions, were used in the radial model. The reactive flow simulator was able to accurately capture wormhole propagation inside the linear core. A greater than 90% match between the experimental and the simulated acid pore volume (PV) to breakthrough (PVBT) was obtained using two acid concentrations’ different temperatures. The simulation results from the radial field-scale model show that the optimal velocity can be higher or lower than those predicted from laboratory experiments. Accordingly, caution must be taken when linear coreflood data are used to predict acid propagation in the field. The simulations showed that traditional upscaling models overpredict acid volumes; the predicted volumes are double at moderate to high injection rates. Models using statistically distributed porosity can provide accurate acid-propagation predictions, with a relative percentage error of less than 25% at extremely high injection rates. This work introduces an accurate model using porosity directly from logs to predict acid performance while avoiding expensive designs. The simulation results reveal that traditional designs overpredict acid volumes required for field treatments. The statistically distributed porosity can be used as a substitute for CT-scan-derived porosity with a low effect on model predictability. The reactive flow simulator can accurately match experimental data.