- journal_title：AAPG Bulletin
- Contributor：Kenneth E. Peters ; L. Scott Ramos ; John E. Zumberge ; Zenon C. Valin ; Christopher R. Scotese ; Donald L. Gautier
- Publisher：American Association of Petroleum Geologists (AAPG)
- Date：2007-
- Format：text/html
- Language：en
- Identifier：10.1306/12290606097
- journal_abbrev：AAPG Bulletin
- issn：0149-1423
- volume：91
- issue：6
- firstpage：877
- section：REGULAR ARTICLES
Source- and age-related biomarker and isotopic data were measured for more than 1000 crude oil samples from wells and seeps collected above approximately 55°N latitude. A unique, multitiered chemometric (multivariate statistical) decision tree was created that allowed automated classification of 31 genetically distinct circum-Arctic oil families based on a training set of 622 oil samples. The method, which we call decision-tree chemometrics, uses principal components analysis and multiple tiers of K-nearest neighbor and SIMCA (soft independent modeling of class analogy) models to classify and assign confidence limits for newly acquired oil samples and source rock extracts. Geochemical data for each oil sample were also used to infer the age, lithology, organic matter input, depositional environment, and identity of its source rock. These results demonstrate the value of large petroleum databases where all samples were analyzed using the same procedures and instrumentation.