Application of benchtop low-field NMR spectrometers in the refining industry: A multivariate calibration approach for rapid characterization of crude oils.

High-field nuclear magnetic resonance (NMR) has proven to be a valuable tool to analyze petroleum products but has never acquired widespread acceptance in routine work in oil refinery due to the high cost. The advent of compact, reliable, and affordable cryogen-free low-field benchtop NMR spectrometers suggest a possible role of such instrument for a variety of uses in refining spanning from routine lab work applications to online analysis. In this work, we report the development of a novel benchtop NMR application to estimate the API gravity, sulfur content, total acidity number (TAN), and distillation yields of crude petroleum from 60 MHz 1 H NMR spectra and partial least square (PLS) regression models. The results obtained on a dataset of over 170 crude oil samples and tested with external validation set show performances in terms of root mean square standard error of prediction (RMSEP) matching the analytical methods' ASTM precision. This novel benchtop NMR and multivariate analysis application can thus be used in typical refinery laboratories to provide within minutes and without physical distillation, reliable estimates of the most important properties usually measured on the crude petroleum during refining. The method proves to be a fast, cheaper alternative to the current analytical options providing readily available data for managing crude oil and has high potential to be further exploited for online application.

Prediction of physical-chemical properties of crude oils by 1H NMR analysis of neat samples and chemometrics.

In this work, we report the feasibility study to predict the properties of neat crude oil samples from 300-MHz NMR spectral data and partial least squares (PLS) regression models. The study was carried out on 64 crude oil samples obtained from 28 different extraction fields and aims at developing a rapid and reliable method for characterizing the crude oil in a fast and cost-effective way. The main properties generally employed for evaluating crudes' quality and behavior during refining were measured and used for calibration and testing of the PLS models. Among these, the UOP characterization factor K (K(UOP)) used to classify crude oils in terms of composition, density (D), total acidity number (TAN), sulfur content (S), and true boiling point (TBP) distillation yields were investigated. Test set validation with an independent set of data was used to evaluate model performance on the basis of standard error of prediction (SEP) statistics. Model performances are particularly good for K(UOP) factor, TAN, and TPB distillation yields, whose standard error of calibration and SEP values match the analytical method precision, while the results obtained for D and S are less accurate but still useful for predictions. Furthermore, a strategy that reduces spectral data preprocessing and sample preparation procedures has been adopted. The models developed with such an ample crude oil set demonstrate that this methodology can be applied with success to modern refining process requirements.