Building a training image with Digital Outcrop Models

•We build an interpreted DOM integrating photogrammetry with terrestrial lidar.•The DOM is the basis for realistic multiple-point statistical training images.•The realization appears to capture realistic facies distributions and geometries. Current standard geostatistical approaches to characterizin...

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Published in:Journal of hydrology (Amsterdam) Vol. 531; pp. 53 - 61
Main Authors: Pickel, A., Frechette, J.D., Comunian, A., Weissmann, G.S.
Format: Journal Article
Language:English
Published: Elsevier B.V 01-12-2015
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Abstract •We build an interpreted DOM integrating photogrammetry with terrestrial lidar.•The DOM is the basis for realistic multiple-point statistical training images.•The realization appears to capture realistic facies distributions and geometries. Current standard geostatistical approaches to characterizing subsurface heterogeneity 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 combination of photogrammetric techniques and manual digitizing 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.
AbstractList •We build an interpreted DOM integrating photogrammetry with terrestrial lidar.•The DOM is the basis for realistic multiple-point statistical training images.•The realization appears to capture realistic facies distributions and geometries. Current standard geostatistical approaches to characterizing subsurface heterogeneity 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 combination of photogrammetric techniques and manual digitizing 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.
Current standard geostatistical approaches to characterizing subsurface heterogeneity 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 combination of photogrammetric techniques and manual digitizing 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.
Author Weissmann, G.S.
Pickel, A.
Frechette, J.D.
Comunian, A.
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  givenname: J.D.
  surname: Frechette
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  givenname: A.
  surname: Comunian
  fullname: Comunian, A.
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  givenname: G.S.
  surname: Weissmann
  fullname: Weissmann, G.S.
  organization: Department of Earth and Planetary Sciences, MSC03-2040, 1 University of New Mexico, Albuquerque, NM 87131-0001, USA
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Keywords Lidar
Digital Outcrop Model
Westwater Canyon Member
Multiple-point statistics
Training images
Photogrammetry
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SSID ssj0000334
Score 2.3335676
Snippet •We build an interpreted DOM integrating photogrammetry with terrestrial lidar.•The DOM is the basis for realistic multiple-point statistical training...
Current standard geostatistical approaches to characterizing subsurface heterogeneity may not capture realistic facies geometries and fluid flow paths....
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elsevier
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StartPage 53
SubjectTerms Construction
Digital imaging
Digital Outcrop Model
Finite element method
Heterogeneity
Lidar
Multiple-point statistics
Outcrops
Photogrammetry
Training
Training images
Westwater Canyon Member
Title Building a training image with Digital Outcrop Models
URI https://dx.doi.org/10.1016/j.jhydrol.2015.08.049
https://search.proquest.com/docview/1751206518
https://search.proquest.com/docview/1778016002
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