Formation damage caused by fine migration and straining is a well-documented phenomenon in sandstone reservoirs. Fine migration and the associated permeability decline have been observed in various experimental studies, and this phenomenon has been broadly explained by the analysis of surface forces between fines and sand grains. The Derjaguin–Landau–Verwey–Overbeek (DLVO) theory is a useful tool to help understand and model the fine release, migration, and control phenomena within porous media by quantifying the total interaction energy of the fine–brine–rock (FBR) system. Fine migration is mainly caused by changes in the attractive and repulsive surface forces, which are triggered by mud invasion during drilling activity, the utilization of completion fluid, acidizing treatment, and water injection into the reservoir during secondary and tertiary recovery operations. Increasing pH and decreasing water salinity collectively affect the attractive and repulsive forces and, at a specific value of pH, and critical salt concentration (CSC), the total interaction energy of the FBR system (V_T) shifts from negative to positive, indicating the initiation of fine release. Maintaining the system pH, setting the salinity above the CSC, tuning the ionic composition of injected water, and using nanoparticles (NPs) are practical options to control fine migration. DLVO modeling elucidates the total interaction energy between fines and sand grains based on the calculation of surface forces of the system. In this context, zeta potential is an important indicator of an increase or decrease in repulsive forces. Using available data, two correlations have been developed to calculate the zeta potential for sandstone reservoirs in high- and low-salinity environments and validated with experimental values. Based on surface force analysis, the CSC is predicted by the DLVO model; it is in close agreement with the experimental value from the literature. The critical pH value is also estimated for alkaline flooding. Model results confirm that the application of NPs and the presence of divalent ions increase the attractive force and help to mitigate the fine migration problem. Hence, a new insight into the analysis of quantified surface forces is presented in current research work by the practical application of the DLVO theory to model fine migration initiation under the influence of injection water chemistry.

Nanoparticles have gained significance in modern science due to their unique characteristics and diverse applications in various fields. Zeta potential is critical in assessing the stability of nanofluids and colloidal systems but measuring it can be time-consuming and challenging. The current research proposes the use of cutting-edge machine learning techniques, including multiple regression analyses (MRAs), support vector machines (SVM), and artificial neural networks (ANNs), to simulate the zeta potential of silica nanofluids and colloidal systems, while accounting for affecting parameters such as nanoparticle size, concentration, pH, temperature, brine salinity, monovalent ion type, and the presence of sand, limestone, or nano-sized fine particles. Zeta potential data from different literature sources were used to develop and train the models using machine learning techniques. Performance indicators were employed to evaluate the models’ predictive capabilities. The correlation coefficient (r) for the ANN, SVM, and MRA models was found to be 0.982, 0.997, and 0.68, respectively. The mean absolute percentage error for the ANN model was 5%, whereas, for the MRA and SVM models, it was greater than 25%. ANN models were more accurate than SVM and MRA models at predicting zeta potential, and the trained ANN model achieved an accuracy of over 97% in zeta potential predictions. ANN models are more accurate and faster at predicting zeta potential than conventional methods. The model developed in this research is the first ever to predict the zeta potential of silica nanofluids, dispersed kaolinite, sand–brine system, and coal dispersions considering several influencing parameters. This approach eliminates the need for time-consuming experimentation and provides a highly accurate and rapid prediction method with broad applications across different fields.

Application of polymer flooding in high temperature–high salinity (HTHS) carbonate reservoirs is challenging due to lack of polymers that can withstand such harsh reservoir conditions. The traditional polymers are usually sensitive to high salinity, especially at high temperature. However, injection of low-salinity make-up brines may precondition high-salinity reservoirs before initiating polymer flooding which may reduce chemical degradation of polymer. This study aims to evaluate the effectiveness of a partially hydrolyzed polyacrylamide base polymer for mobility control application in a low-salinity preconditioned carbonate reservoir and hence on the improvement of oil recovery at HTHS carbonate reservoir. Core flooding experiments using unsteady-state technique were conducted on reservoir cores with permeability range of 10–100 mD. During each experiments, salinity of the make-up brines were changed to study the effect of preflush salinity and polymer flooding in HTHS reservoir. Oil production from water flooding for all the cases was found to be between 49 and 65%. Polymer helped to reduce the mobility ratio from 4.1 to less than 1 and additional 7–11% of oil was recovered from the remaining oil saturation after water flooding. Comparisons were also made between oil recovery results based on volumetric production and in situ saturation monitoring (ISSM) data, which were found to be matching. Additionally, the ISSM helped to understand the performances of fluids injected during oil recovery stages and captured front movement of the fluids at all time. Also, high capillary end effect was confirmed from the ISSM which may lead to underestimation of the oil recovery from water flooding in the absence of ISSM. Resistance factor and residual resistance factor were also calculated during all core flooding experiments and were found to be 7.0, 2.4, 36 and 3.7, 1.4, 8.9, respectively.

High quality polymer free CO₂ foam possesses unique properties that make it an ideal fluid for fracturing unconventional shales. In this paper, the viscosity of polymer free fracturing foam and its empirical correlations at high pressure high temperature (HPHT) as a function of surfactant concentration, salinity, and shear rate are presented. Foams were generated using a widely-used surfactant, i.e., alpha olefin sulfonate (AOS) in the presence of brine and a stabilizer at HPHT. Pressurize foam rheometer was used to find out the viscosity of CO₂ foams at different surfactant concentration (0.25–1 wt %) and salinity (0.5–8 wt %) over a wide range of shear rate (10–500 s⁻¹) at 1500 psi and 80 °C. Experimental results concluded that foam apparent viscosity increases noticeably until the surfactant concentration of 0.5 wt %, whereas, the increment in salinity provided a continuous increase in foam apparent viscosity. Nonlinear regression was performed on experimental data and empirical correlations were developed. Power law model for foam viscosity was modified to accommodate for the effect of shear rate, surfactant concentration, and salinity. Power law indices (K and n) were found to be a strong function of surfactant concentration and salinity. The new correlations accurately predict the foam apparent viscosity under various stimulation scenarios and these can be used for fracture simulation modeling.