Date of Award

Spring 1-1-2013

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Geological Sciences

First Advisor

Matthew J. Pranter

Second Advisor

Rex D. Cole

Third Advisor

Edmund R. Gustason III

Abstract

Statistical classification methods consisting of the k-nearest neighbor algorithm (k-NN), a probabilistic clustering procedure (PCP), and a novel method which incorporates outcrop-based thickness criteria through the use of well-log-indicator flags are evaluated for their ability to distinguish the fluvial architectural elements of the upper Mesaverde Group of the Piceance and Uinta basins as distinct electrofacies classes. Study data utilized in the training and testing of the classification methods come from 1626 wireline-log curve depth samples each associated with a known architectural-element classification as determined from detailed sedimentologic analysis of cores (N=9). Thickness criteria used in this study are derived from outcrop-based architectural-element measurements made by previous workers of the upper Mesaverde Group. Through an approach which integrates select classifier results with thickness criteria, an overall accuracy (number of correctly predicted samples/total testing samples) of 83.6% was achieved for a simplified four-class architectural-element realization. Architectural elements were predicted with user's accuracies (accuracy of an individual class) of 0.891, 0.376, 0.735, and 0.985 for the floodplain, crevasse splay, single-story channel body, and multi-story channel body classes, respectively. Without the additional refinement allowed by the incorporation of thickness criteria, the k-NN and PCP classifiers produced similar results, with the k-NN technique consistently outperforming the PCP technique by a slight margin. In both the k-NN and PCP techniques, the combination of wire-line log curves GR and RHOB proved to be the most useful assemblage tested.

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