Mechanistic Modeling of Nanoparticles-Assisted Surfactant Flood

Abstract

High interfacial tension (IFT) between oil and water brings about high capillarity leading to high residual oil saturation. Surfactants are employed to reduce IFT or modify wettability and mobilize the trapped oil. This paper aims to investigate the interaction of sodium dodecyl sulfate as a surfactant and two types of silica nanoparticles in different particle sizes for the purpose of enhancing oil recovery. Accordingly, the effect of employed nanoparticles on the critical micelle concentration (CMC) of the surfactant was investigated by the use of electrical conductivity measurements. Phase behavior studies were also carried out to examine the solubilizing ability of the surfactant and nanoparticles assembly. Based on the analysis of solubilization curves, an ultra-low IFT chemical formulation for the target reservoir crude oil was identified and the stability of the optimum solutions was examined through visual observation, optical absorption, and zeta potential measurements. The oil recovery experiments were performed in a quarter five-spot transparent pore network model saturated with crude oil to observe the displacement behavior of the injectant and its influence on oil recovery. Phase behavior tests indicated that the silica nanoparticles smaller in size are more effective in terms of IFT reduction since they can achieve ultra-low IFT level, and the conductivity measurements showed they relatively reduce the CMC of the surfactant. The results of stability tests demonstrated the optimum solutions are stable for more than 1 week. The micromodel experiments displayed that oil recovery increased by 4% during nanoparticles-assisted surfactant flood in comparison with surfactant flood.