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Research has been carried out to determine the potential of partial least squares (PLS) modeling of mid-infrared (IR) spectra of crude oils combined with the corresponding 1H and 13C nuclear magnetic resonance (NMR) data, to predict the long residue (LR) properties of these substances. The study elaborates further on a recently developed and patented method to predict this type of information from only IR spectra. In the present study, PLS modeling was carried out for 7 different LR properties, i.e., yield long-on-crude (YLC), density (DLR), viscosity (VLR), sulfur content (S), pour point (PP), asphaltenes (Asph) and carbon residue (CR). Research was based on the spectra of 48 crude oil samples of which 28 were used to build the PLS models and the remaining 20 for validation. For each property, PLS modeling was carried out on single type IR, 13C NMR and 1H NMR spectra and on 3 sets of merged spectra, i.e., IR + 1H NMR, IR + 13C NMR and IR + 1H NMR + 13C NMR. The merged spectra were created by considering the NMR data as a scaled extension of the IR spectral region. In addition, PLS modeling of coupled spectra was performed after a Principal Component Analysis (PCA) of the IR, 13C NMR and 1H NMR calibration sets. For these models, the 10 most relevant PCA scores of each set were concatenated and scaled prior to PLS modeling. The validation results of the individual IR models, expressed as root-mean-square-error-of-prediction (RMSEP) values, turned out to be slightly better than those obtained for the models using single input 13C NMR or 1H NMR data. For the models based on IR spectra combined with NMR data, a significant improvement of the RMSEP values was not observed neither for the models based on merged spectra nor for those based on the PCA scores. It implies, that the commonly accepted complementary character of NMR and IR is, at least for the crude oil and bitumen samples under study, not reflected in the results of PLS modeling. Regarding these results, the absence of sample preparation and the straightforward way of data acquisition, IR spectroscopy is preferred over NMR for the prediction of LR properties of crude oils at site.

Research has been carried out to determine the feasibility of partial least-squares (PLS) regression models to predict the long-residue (LR) properties of potential blends from infrared (IR) spectra that have been created by linearly co-adding the IR spectra of crude oils. The study is the follow-up of a recently developed method to predict LR and short-residue properties from IR spectra and which is currently the subject of PCT patent application WO 2008/135411 filed by Shell International Research Maatschappij B.V. It is found that the PLS prediction models for seven different LR properties [i.e., yield long on crude (YLC), density (DLR), viscosity (VLR), sulfur content (S), pour point (PP), asphaltenes (Asph), and carbon residue (CR)] enabled us to predict the LR properties of 16 blends in two ways. The first predictions were carried out on the IR spectra recorded from the physically prepared blend samples. Next, IR spectra were submitted to the PLS models that were created mathematically by linearly co-adding the IR spectra of the corresponding crude oils in the appropriate weight ratio. Minor differences in the real and artificial blend spectra have been observed, which have been assigned to nonlinear effects. However, preprocessing of the spectra, by subsequently taking the first derivative, multiplicative scatter correction (MSC), and mean centering (MC), resulted in predicted LR property values of the two parallel sets that are largely the same. It implies that mimicking blend spectra by mathematically mixing the IR spectra of crude oils is a valuable, fast, clean, and cheap alternative for the “dirty” and elaborate preparation and testing methods of real blends currently used in the laboratory. Besides, the method can be used as a rapid screening tool for a large series of potential blends.

Research has been carried out to determine the feasibility of chemometric modeling of infrared (IR) and near-infrared (NIR) spectra of crude oils to predict the long residue (LR) and short residue (SR) properties of these samples. A novel method is described to predict short residue properties at different flashing temperatures based on the IR spectrum of a crude oil measured at room temperature. The resulting method is the subject of European patent application number 07251853.3 filed by Shell Internationale Research Maatschappij B.V. The study has been carried out on 47 crude oils and 4 blends, representing a large variety of physical and chemical properties. From this set, 28 representative samples were selected by principle component analysis (PCA) and used for calibration. The remaining 23 samples were used as a test set to validate the obtained partial least squares (PLS) regression models. The results demonstrate that this integrated approach offers a fast and viable alternative for the currently applied elaborate ASTM (American Society for Testing and Materials) and IP (Institute of Petroleum) methods. IR spectra, in particular, were found to be a useful input for the prediction of different LR properties. Root mean square error of prediction values of the same order of magnitude as the reproducibility values of the ASTM methods were obtained for yield long on crude (YLC), density (DLR), viscosity (VLR), and pour point (PP) , while the ability to predict the sulfur contents (S) and the carbon residue (CR) was found to be useful for indicative purposes. The prediction of SR properties is also promising. Modeling of the IR spectra, and to a lesser extent, the NIR spectra as a function of the average flash temperature (AFT) was particularly successful for the prediction of the short residue properties density (DSR) and viscosity (VSR). Similar results were obtained from the models to predict SR properties as a function of the yield short on crude (YSC) values. Finally, it was concluded that the applied protocol including sample pretreatment and instrumental measurement is highly reproducible and instrument and accessory independent.