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Prediction of the Insolubility Number and the Solubility Blending Number of Colombian Heavy Crude Oils by 1 H Nuclear Magnetic Resonance and Partial Least Squares

2019; American Chemical Society; Volume: 34; Issue: 2 Linguagem: Inglês

10.1021/acs.energyfuels.9b03720

1520-5029

Andrea Castillo M., Alejandra Páez A, Hoover Rueda-Chacón, José Luis Agudelo, Daniel Molina,

Hydrocarbon exploration and reservoir analysis

Different indexes have been proposed in the literature to measure the stability and compatibility of crude oil blends, including the insolubility number (IN), which measures the degree of asphaltene insolubility, and the solubility blending number (SBN), which measures the ability of the oil to dissolve asphaltenes. In this work, various chemometric models were developed to predict the IN and SBN values of Colombian heavy crude oils (°API from 6 to 27), in which the integral areas of the resonance signals, from 12 regions of 1H nuclear magnetic resonance (1H NMR) spectra, were correlated with their IN and SBN. Correlations between the 1H NMR spectra and the said properties were found via partial least squares (PLS) regression so as to create the predictive models. Coefficients of determination (R2) above 0.92 and cross-validated (CV) predictive correlation coefficients (qcv2) above 0.86 were attained with the developed PLS prediction models for IN and SBN. The use of these NMR-based predictive methods entails a faster estimation of the stability and compatibility of crude oil blends and a more eco-friendly and cheaper methodology compared to conventional methods. From the 1H NMR data, it is observed that crude oils with a low tendency to precipitate asphaltenes (high SBN) are those with a high aromatic content and a low content of paraffins.