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

Abstract

Different indexes have been proposed in the literature to measure the stability and compatibility of crude oil blends, including the insolubility number (I N ), which measures the degree of asphaltene insolubility, and the solubility blending number (S BN ), which measures the ability of the oil to dissolve asphaltenes. In this work, various chemometric models were developed to predict the I N and S BN values of Colombian heavy crude oils (°API from 6 to 27), in which the integral areas of the resonance signals, from 12 regions of 1 H nuclear magnetic resonance ( 1 H NMR) spectra, were correlated with their I N and S BN . Correlations between the 1 H NMR spectra and the said properties were found via partial least squares (PLS) regression so as to create the predictive models. Coefficients of determination (R 2 ) above 0.92 and cross-validated (CV) predictive correlation coefficients (q cv 2 ) above 0.86 were attained with the developed PLS prediction models for I N and S BN . 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 1 H NMR data, it is observed that crude oils with a low tendency to precipitate asphaltenes (high S BN ) are those with a high aromatic content and a low content of paraffins.

Compatibility of Crude Oil Blends─Processing Issues Related to Asphaltene Precipitation, Methods of Instability Prediction─A Review

“…Attempts were undertaken to determine the two parameters by methods other than the instrumental method using a large amount of hazardous solvents. The use of nuclear magnetic resonance (NMR) can serve as an example …”

“…The use of nuclear magnetic resonance (NMR) can serve as an example. 25 Although OCM concerns the characteristics of individual crude oils, Irvin A. Wiehe proposed a criterion using I N and S BN to determine the limiting fractions of crude oils in a blend. 23,24 The criterion states that for compatibility the solubility blending number of the mixture of oils must be higher than the insolubility number of any oil in the mixture.…”

Application of Intercriteria and Regression Analyses and Artificial Neural Network to Investigate the Relation of Crude Oil Assay Data to Oil Compatibility

“…Processes 2024, 12, x FOR PEER REVIEW 13 o classifications of petroleum origin coming from the diverse kerogen types [89][90][91][92][93][94][95][96][97][98][99][100][101][102], sh ing that the various kerogens can provide different conversion levels and different se tivities [92]. Similar to the different colloidal stabilities of the n-heptane insoluble fract of the unconverted hydrocracked vacuum residues obtained from diverse crude [11,73,103], the colloidal stability of the n-heptane insoluble fraction of the various cr oils can also be different [43,44]. Thus, one may expect that the crude oils formed fr distinct kerogen types may have different stabilities of their n-heptane insoluble fract Another factor that can influence the oil colloidal stability in both crude oil and vacu residue hydrocracked synthetic crude oil is the maturity/conversion levels.…”

“…Having in mind that the artificial neural network (ANN) approach to model different oil properties reported a higher accuracy of prediction than that of the regression methods [106][107][108][109][110][111][112][113], we decided to develop an ANN model to predict the Sp critical of petroleum fluids. For this purpose, we used more data than those listed in Table 2 and collected data on 110 crude oils and residues from the literature [34,[42][43][44], as well as some additional unpublished data of our own. The range of the variation in the oil properties used to model the Sp critical by ANN is summarized in Table 8.…”

Reconstruction of a Synthetic Crude Oil Using Petroleomics and Molecular Dynamics Simulations: A Multistructural Approach to Understanding Asphaltene Aggregation Behavior

“…Each of these fractions has a particular solubility in paraffinic (such as heptane), aromatic (such as toluene), and polar (such as dichloromethane) solvents. Depending on the proportions of these fractions as well as its tendency to precipitate organic scales, it is possible to classify a crude oil as stable or unstable. , Regardless of the oil, asphaltenes present the greatest challenge due to their influence on the general behavior of crude oil, such as its emulsifying tendency, the precipitation of organic scales, and its viscosity. , Asphaltenes are difficult to characterize because they are defined in terms of their solubility and not their composition. For example, different “asphaltenes” can be obtained from the same crude oil simply by changing the precipitant used. , Several techniques have been employed in asphaltene characterization, such as infrared spectroscopy (IR), − matrix-assisted laser desorption/ionization mass spectrometry (MALDI/TOF), ultrasmall-angle X-ray scattering, X-ray diffraction (XRD), nuclear magnetic resonance (NMR), Fourier transform-ion cyclotron resonance-mass spectrometry (FT-ICR-MS), , and atomic force microscopy (AFM) .…”