Combining indicators analysis and chemometrics to trace the geographical origin of crude oil

Abstract

Geographic traceability is crucial to global oil trade security. This study discusses the possibility of using multivariate statistical methods combined with multi-indicator analysis to identify samples of crude oil imports from five major countries to China. The physicochemical properties and trace elements of crude oil were detected by Petroleum product standards and inductively coupled plasma atomic emission spectrometry (ICP-AES). Eight indexes (moisture, density, sulfur content, acid value, organochlorine, carbon residual, V, and Ni) were analyzed. Principal component analysis (PCA), hierarchical clustering analysis (HCA), Orthogonal projections to lateen structures-discriminant analysis (OPLS-DA), and other multivariate data analysis methods were used to determine the geographical origin of crude oil samples. Satisfying results have been obtained using PCA to reduce the dimensions of the indicators of crude oil from different origins. It allows the reduction of 8 variables to 3 principal components and accounts for 80.06% of the total variance. The HCA shows five clusters corresponding to five sources of crude oil. This will help to improve the utilization rate of crude oil with different characteristics, improve the quality of crude oil trade, and ensure the high quality of crude oil trade. For the sample set used for modeling, the model's accuracy was 97.19% after OPLS-DA optimization. These results show that the combination of multi-index analysis and stoichiometry is an effective tool for identifying crude oil origin, which fills the technical gap in the rapid identification of crude oil origin.