Masters Theses

Date of Award

12-2001

Degree Type

Thesis

Degree Name

Master of Science

Major

Geology

Major Professor

William M. Dunne, Matthew Mauldon

Committee Members

Robert D. Hatcher, Jr.

Abstract

Mechanistic and probabilistic methods have been individually used to characterize and predict joint networks. We feel that combining these two approaches has potential benefit because it overcomes uncertainties associated with mechanics-based models while limiting the probabilistic outcomes with mechanical rules. For this approach, profiles of bed-normal joints were characterized not with fracture trace geometries, but rather with intersection geometries to bedding. T-intersections represent joint termination at bedding, X-intersections represent joints crossing bedding, and E-intersections are those intersections at the sample window edge. Using the intersection counts as input, a new computer program was developed that uses mechanically constrained probabilities to simulate and predict the spatial distribution of bed-normal joints in profiles across bedding. Initially, simulation predictions were compared to ideal joint geometries for one or two lithologies with one or two bed thickness values, and found to match well. Simulation predictions were then compared to joint geometries in five natural profiles from Llantwit Major, Wales, and Huntingdon, PA. Though predictions have variability, they visually resemble the natural profiles and reasonably match the natural values of apparent density, apparent mean tracelength, and intensity. We also considered the issue of extending the methodology to predicting joint networks beyond sample regions by investigating the minimum count of intersections need to produce a representative result. Based on the five natural profiles, which have typical joint geometries, a sample size of about 50-100 intersection counts is sufficient to produce a reasonable prediction of the expected count.

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