Oil chemometrics and geochemical correlation in the Weixinan Sag, Beibuwan Basin, South China Sea

Yao-Ping Wang,Tao Luo,Jia Xia,Yuan Gao,Xin Zhan,Sibo Wang,Yan-Rong Zou

doi:10.1177/0144598720950467

Abstract

The oil–oil and oil–source rock correlations, also termed as geochemical correlations, play an essential role in the construction of petroleum systems, guidance of petroleum exploration, and definition of reservoir compartments. In this study, the problems arising from oil–oil and oil–source rock correlations were investigated using chemometric methods on oil and source rock samples from the WZ12 oil field in the Weixinan sag in the Beibuwan Basin. Crude oil from the WZ12 oil field can be classified into two genetic families: group A and B, using multidimensional scaling and principal component analysis. Similarly, source rocks of the Liushagang Formation, including its first, second, and third members, can be classified into group I and II, corresponding to group B and A crude oils, respectively. The principle geochemical parameters in the geochemical correlation for the characterisation and classification of crude oils and source rocks were 4MSI, C27Dia/C27S, and C24 Tet/C26 TT. This study provides insights into the selection of appropriate geochemical parameters for oil–oil and oil–source rock correlations, which can also be applied to other sedimentary basins.

Highlights

Statistical and mathematical methods have been essential tools in analytical chemistry for analysing data and recognising patterns (Ferreira et al, 2018; Frank et al, 1981; Kumar et al, 2014)
We selected a newly introduced chemometric method—multidimensional scaling (Wang et al, 2016, 2018a) and principal component analysis (PCA)—to solve the problems of the oil–oil and oil–source rock correlations in the study area
Source rocks in the Liushagang Formation can be divided into two groups: group I and II, based on the geochemical characteristics and the results of multidimensional scaling (MDS)

Summary

Introduction

Statistical and mathematical methods have been essential tools in analytical chemistry for analysing data and recognising patterns (Ferreira et al, 2018; Frank et al, 1981; Kumar et al, 2014). The chemometric method can simultaneously process the measured parameters or variables based on multivariate statistical treatments. This method has been widely used in several disciplines after its development in only a few decades (Bevilacqua et al, 2017; Chabukdhara and Nema, 2012; Madsen et al, 2010; Wang et al, 2020); its application in the field of petroleum geochemistry began in the 1980s (Kvalheim et al, 1985; Øygard et al, 1984; Peters et al, 1986; Telnaes and Dahl, 1986; Zumberge, 1987). Chemometric methods used in the study of oil -oil and oil -source rock correlations include R- and Qmodel factor analysis (Sofer, 1984; Zumberge, 1987; Engel et al, 1988; Chakhmakhchev et al, 1996; Scotchman et al, 1998), star diagram (Justwan et al, 2006; Mashhadi and Rabbani, 2015), K-nearest neighbour (Peters et al, 2000, 2007, 2008), multidimensional scaling (MDS) (Wang et al, 2016; Wang et al, 2018a), discriminant analysis (Zhang et al, 2019), and t-distributed stochastic neighbor embedding (Tao et al, 2020)

Methods

Results

Conclusion