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Application of Petrographic Image Analysis and Multivariate Statistical Techniques to Textural Studies of Oil Sand Samples

Bell, Julie D.*1; Eruteya, Ovie E.1; Oono, Oko 1
(1) Petroleum Engineering, London South Bank University, London, United Kingdom.

Notably, Canadian oil sands are hosted within a quartz rich unconsolidated material and understanding the fabrics and textural characteristics is crucial to in-situ heavy oil production processes. This study is aimed at determining the characteristics of oil sand samples from the Upper McMurray Formation, more specifically from the estuarine depositional environment. Standard thin sections were prepared from outcrop samples collected from the Hangingstone River area near Athabasca, Canada. The thin sections were examined using an integrated petrographic image analysis system consisting of a high resolution petrographic microscope adapted with a digital camera for image acquisition and a commercial image analysis software package for image processing. The image analysis software was used to measure fundamental textural properties observed in thin section using the modified Griffiths properties measurement rule P=(m,s,sh,o,p). Data sets were generated from the petrographic image analysis by means of point counting variables such as grain morphology, micromass, pore geometry and bitumen content. An estimate of grain sorting was derived from observing the spatial arrangements of the coarse and fine components along with the pores. The data sets were subject to multivariate statistical analysis using principal component analysis (PCA) and hierarchical cluster analysis (HCA) methods. From the PCA, the variable that contributed significantly to the textural fabric was the quartz content. The HCA showed that multi-groups existed based on variations in textural properties. Quartz grains were arranged in a matrix of micromass with vuggy pores and varying amounts of bitumen throughout. The micromass consisted of both silt and clay sized material and ranged from dark to light brown with some mixture of bitumen which was mostly very dark brown. Multivariate statistical techniques provide an important tool for grain morphology studies and delineating relationships between textural features of oil sand samples. Integrated studies of this type could aid in locating sweet spots along production wells for enhanced heavy oil recovery processes.


AAPG Search and Discovery Article #90142 © 2012 AAPG Annual Convention and Exhibition, April 22-25, 2012, Long Beach, California