Prediction of Optimal Salinities for Surfactant Formulations Using a Quantitative Structure–Property Relationships Approach

Each oil reservoir could be characterized by a set of parameters such as temperature, pressure, oil composition, and brine salinity, etc. In the context of the chemical enhanced oil recovery (EOR), the selection of high performance surfactants is a challenging and time-consuming task since this strongly depends on the reservoir’s conditions. The situation becomes even more complicated if the surfactant formulation is a blend of two or more surfactants. In the present work, we report quantitative structure–property relationships (QSPR) correlating surfactants’ structures and their composition in a mixture with optimal salinity (Sₒₚₜ), corresponding to minimal interfacial tension in the reference brine/surfactants/n-dodecane system, at T = 313 K and P = 0.1 MPa. Particular attention was paid to selected families of surfactants: α-olefin sulfonate (AOS), internal olefin sulfonate (IOS), alkyl ether sulfate (AES), and alkyl glyceryl ether sulfonate (AGES). The models were built and validated on the database containing Sₒₚₜ values for 75 surfactants’ formulations. Molecular structures of amphiphilic molecules were encoded by functional group count descriptors (FGCD), ISIDA substructural molecular fragment (SMF) descriptors, and CODESSA molecular descriptors (CMD). For mixtures, descriptors were calculated as linear combinations of descriptors of individual compounds weighted by their mass fractions in mixtures. Different machine-learning methods—support vector machine (SVM), partial least-squares (PLS) regression, and random subspace (RS)—have been used for the modeling. Both global (on the entire database) and local (on individual families) models have been built. Models display reasonable accuracy (about 0.2 log Sₒₚₜ units) which is comparable with the experimental error of measured Sₒₚₜ. Our results show that the suggested approach can be successfully used to build predictive models for relatively small data sets of mixtures of chemical compounds.