Modeling Oil-Alcohol Interactions for Optimum Salinity Determination in Microemulsion Systems

Surfactant flooding requires an accurate prediction of the optimum salinity at which interfacial tension is ultralow, thereby maximizing oil recovery. The effect of alcohol in surfactant-alcohol mixtures is complex, yet conventional correlations treat alcohol effects implicitly through a lumped function, f(A), and assume alcohol and oil alkane carbon number (ACN) contributions are independent and additive. This paper examines whether ACN-alcohol interactions exist across different surfactant-alcohol systems and shows how to model optimum salinity for a surfactant-oil-water (SOW) system with one or two alcohols with ACN interactions. Five optimum salinity datasets from literature for SDS/n-butanol, sodium dodecylbenzenesulfonate (SDBS)/n-pentanol, TRS 10-80, and Siponate DS-10 surfactants with iso-pentanol/2-butanol, were analyzed and compared using linear additive and multiplicative interaction models. All datasets were analyzed under fixed temperature and ambient pressure (14.7 psi) to assess interactions. Results for all SOW systems and alcohols confirm that interactions between alcohol concentration and ACN are significant and are linear. The use of the multiplicative interaction model reduces prediction errors by 30-67% compared to using linear additive models and conveniently allows for the accurate determination of optimum salinity for any alcohol concentration. For systems with two alcohols, the model separates the contribution of each alcohol and its interaction with ACN. Additionally, the alcohol chain length-ACN interaction was analyzed; however, it was not significant in the system studied. Explicitly incorporating alcohol concentration eliminates the cumbersome f(A) function and provides a more general and accurate model for optimum salinity prediction in surfactant flood simulation and design.