Multiplicative interaction model for optimum salinity in surfactant-alcohol-oil-water systems

Accurate estimation of the optimum salinity is critical in many applications, including the design of effective surfactant flooding formulations. Although alcohol improves surfactant solubilization and interfacial behavior, its role is often simplified in existing optimum salinity correlations. This paper introduces an optimum salinity model that explicitly accounts for the alcohol volume fraction in surfactant–alcohol mixtures through multiplicative interaction coefficients among key formulation variables, including oil alkane carbon number (ACN), temperature (T), and alcohol fraction (C _A ). We conducted over 120 salinity scans (48 primary and 72 repetitive scans) using sodium dodecyl sulfate (SDS) (4.29 wt%) and n-butanol (17.16 wt%, 10.73 wt%, and 8.58 wt%) with a series of pure alkanes (n-heptane, n-octane, n-decane, and n-dodecane) across four temperatures of 21, 30, 45, and 60°C at a fixed water-oil ratio of one. Optimum salinities (S*) were determined from unbiased linear fits of the inverse of the three-phase solubilization data. Linear relationships are observed between ln S* and ACN , T , and C _A . Although relationships are linear, the slopes were not always constant as other formulation variables changed, indicating potential interactions among C _A , ACN , and T . Accordingly, we developed a multiplicative interaction model for optimum salinity, including all interaction terms to account for potential synergistic behavior. The results show a significant interaction between C_A and ACN and a slight-to-moderate interaction between C_A and T . All other interactions were negligible. Including these interactions improves the global fit of measured optimum salinity data by 23 % compared to the conventional linear additive model. Furthermore, errors of 100 % in the estimated optimum salinity are possible for alcohol fractions outside the range of experimental data.