Investigation on flow resistance reduction and EOR mechanisms by activated silica nanofluids: Merging microfluidic experimental and CFD modeling approaches

Activated silica nanofluids (ASN) flooding has been proven to be an effective method to enhance oil recovery. However, due to the variety of nanoparticles and surfactants used for ASN synthesis, the main mechanisms of flow resistance reduction and oil recovery enhancement by ASN are still unclear, and most studies are based on physical experiments, which are too cumbersome and inefficient. In this study, the ASN flooding experiment and CFD modeling are combined based on microscopic visualization experiments. Firstly, three kinds of ASN with different hydrodynamic diameters were synthesized by BS-12 and nano-silica sol, and the basic properties were tested to obtain the modeling parameters. Surface flow experiments were also carried out. Then, a microscopic model based on the real pore-throat size was developed, and combined with image processing technology, the quantitative study and EOR mechanism analysis of the ASN flooding process was carried out. Finally, CFD modeling was carried out based on microscopic visualization experiments to predict the recovery improvement after improving ASN performance. The results show that at the optimal concentration of 1%, the ASN with a smaller hydrodynamic diameter performs better in wettability alteration and reducing interfacial tension and viscosity ratio. ASN can significantly reduce the flow resistance coefficient by 30.36% to 95.43%. The reduction of micro-resistances is the important EOR mechanism of ASN. The results of the microscopic visualization experiment and CFD simulation are compared, and errors ranged from 3.9% to 5.22% for recovery factors in various injection scenarios. The prediction results by CFD simulation show that the viscosity reduction ability has the most significant effect on the recovery factor. The best performance parameters of ASN under the highest recovery factor are also predicted. Simulation results guide the selection of surfactants and nanoparticles in the subsequent ASN synthesis process, which has the advantages of high efficiency and low cost.