Zhang Wanfeng, Zhu Shukui, He Sheng, Wang Yanxin
State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, China; Key Laboratory of Tectonics and Petroleum Resources of Ministry of Education, China University of Geosciences, Wuhan 430074, China.
State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, China; Key Laboratory of Tectonics and Petroleum Resources of Ministry of Education, China University of Geosciences, Wuhan 430074, China.
J Chromatogr A. 2015 Feb 6;1380:162-70. doi: 10.1016/j.chroma.2014.12.068. Epub 2015 Jan 2.
Using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC/TOFMS), volatile and semi-volatile organic compounds in crude oil samples from different reservoirs or regions were analyzed for the development of a molecular fingerprint database. Based on the GC×GC/TOFMS fingerprints of crude oils, principal component analysis (PCA) and cluster analysis were used to distinguish the oil sources and find biomarkers. As a supervised technique, the geological characteristics of crude oils, including thermal maturity, sedimentary environment etc., are assigned to the principal components. The results show that tri-aromatic steroid (TAS) series are the suitable marker compounds in crude oils for the oil screening, and the relative abundances of individual TAS compounds have excellent correlation with oil sources. In order to correct the effects of some other external factors except oil sources, the variables were defined as the content ratio of some target compounds and 13 parameters were proposed for the screening of oil sources. With the developed model, the crude oils were easily discriminated, and the result is in good agreement with the practical geological setting.
利用全二维气相色谱-飞行时间质谱联用仪(GC×GC/TOFMS),对来自不同油藏或地区的原油样品中的挥发性和半挥发性有机化合物进行分析,以建立分子指纹数据库。基于原油的GC×GC/TOFMS指纹图谱,采用主成分分析(PCA)和聚类分析来区分油源并寻找生物标志物。作为一种监督技术,原油的地质特征,包括热成熟度、沉积环境等,被赋予主成分。结果表明,三芳甾烷(TAS)系列是原油中适合进行油源筛选的标志物化合物,单个TAS化合物的相对丰度与油源具有良好的相关性。为了校正除油源外其他一些外部因素的影响,将变量定义为某些目标化合物的含量比,并提出了13个参数用于油源筛选。利用所建立的模型,能够轻松区分原油,结果与实际地质情况吻合良好。