BUILDING A BETTER TRAINING IMAGE WITH DIGITAL OUTCROP MODELS: THESE GO TO ELEVEN

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.

Name thesis:degree_name

Earth and Planetary Sciences

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Department of Earth and Planetary Sciences

Year

2015

Author dc:creator

• Pickel, Alexandra

Contributors dc:contributor

• Weissmann, Gary
• Scuderi, Louis
• Thomson, Bruce

• Digital Outcrop Model
• DOM
• Training Image
• TI
• Multiple-point statistics
• MPS
• Lidar
• Westwater Canyon Member
• outcrop analog

Language dc:language

English

Identifier

https://digitalrepository.unm.edu/eps_etds/63

OAI identifier oai:identifier

oai:digitalrepository.unm.edu:eps_etds-1062