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

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Author: Muller, C. · Maldonado, A.G. · Varnek, A. · Creton, B.
Type:article
Date:2015
Source:Energy and Fuels, 7, 29, 4281-4288
Identifier: 527786
doi: doi:10.1021/acs.energyfuels.5b00825
Keywords: Energy · Artificial intelligence · Blending · Chemical compounds · Enhanced recovery · Ethers · Learning systems · Least squares approximations · Mixtures · Oil well flooding · Olefins · Petroleum reservoir engineering · Petroleum reservoirs · Support vector machines · Alpha olefin sulfonates · Chemical enhanced oil recoveries · Internal olefin sulfonates · Machine learning methods · Molecular descriptors · Partial least-squares regression · Quantitative structure property relationships · Surfactant formulation · Surface active agents · Life · RAPID - Risk Analysis for Products in Development · ELSS - Earth, Life and Social Sciences

Abstract

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 (S<inf>opt</inf>), 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 S<inf>opt</inf> 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 S<inf>opt</inf> units) which is comparable with the experimental error of measured S<inf>opt</inf>. 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. © 2015 American Chemical Society.