De-Convoluting
Mixed Crude Oils in Prudhoe Bay Field Using Chemometrics
Peters, Kenneth E.1, L. Scott
Ramos2, John E. Zumberge3, Zenon
C. Valin1, Kenneth J. Bird1 (1) U.S. Geological Survey,
Menlo Park, CA (2) Infometrix, Inc, Bothell, WA (3) GeoMark Research, Inc, Houston, TX
Chemometrics is multivariate
statistics applied to chemical problems. It is a powerful tool for genetic
classification, correlation, prediction of physicochemical properties, and
de-convolution of mixtures. Source- and age-related biomarker and isotopic data
were measured for >1000 crude oil samples from wells and seeps north of ~55oN
latitude. A unique, multi-tiered chemometric decision
tree was created that allowed automated classification of 31 genetically
distinct Circum-Arctic oil families. This new method, which we call
decision-tree chemometrics, utilizes PCA (principal
components analysis) and many tiered KNN (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. The data identify nine petroleum systems on the North Slope of Alaska.
Crude oils from the giant Prudhoe Bay Field were studied to assess the relative
volumetric contributions from different source rocks. Contrary to conventional
interpretations, the results of ALS (alternating least squares) analysis show
that oils in the field originate mainly from Cretaceous Hue Shale rather than
Triassic Shublik Formation source rock. These results
were confirmed using both ratios and concentrations of biomarkers. PLS (partial
least squares) analysis of the biomarker and isotope data allows accurate
predictions of the physicochemical properties of these oil samples, such as
sulfur content.
AAPG Search and Discover Article #90063©2007 AAPG Annual Convention, Long Beach, California