Earth and Planetary Sciences ETDs
Publication Date
7-1-2015
Abstract
Current standard geostatistical approaches to subsurface heterogeneity studies may not capture realistic facies geometries and fluid flow paths. Multiple-point statistics (MPS) has shown promise in portraying complex geometries realistically; however, realizations are limited by the reliability of the model of heterogeneity upon which MPS relies, that is the Training Image (TI). Attempting to increase realism captured in TIs, a quantitative outcrop analog-based approach utilizing terrestrial lidar and high-resolution, calibrated digital photography is combined with lithofacies analysis to produce TIs. Terrestrial lidar scans and high-resolution digital imagery were acquired of a Westwater Canyon Member, Morrison Formation outcrop in Ojito Wilderness, New Mexico, USA. The resulting point cloud was used to develop a cm scale mesh. Digital images of the outcrop were processed through a series of photogrammetric techniques to delineate different facies and sedimentary structures. The classified images were projected onto the high-resolution mesh creating a physically plausible Digital Outcrop Model (DOM), portions of which were used to build MPS TIs. The resulting MPS realization appears to capture realistic geometries of the deposit and empirically honors facies distributions.
Degree Name
Earth and Planetary Sciences
Level of Degree
Masters
Department Name
Department of Earth and Planetary Sciences
First Committee Member (Chair)
Scuderi, Louis
Second Committee Member
Thomson, Bruce
Project Sponsors
Strategic Environmental Research and Development Program
Language
English
Keywords
Digital Outcrop Model, DOM, Training Image, TI, Multiple-point statistics, MPS, Lidar, Westwater Canyon Member, outcrop analog
Document Type
Thesis
Recommended Citation
Pickel, Alexandra. "BUILDING A BETTER TRAINING IMAGE WITH DIGITAL OUTCROP MODELS: THESE GO TO ELEVEN." (2015). https://digitalrepository.unm.edu/eps_etds/63