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Permanent link (DOI): https://doi.org/10.7939/R3FW98

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Improved facies modelling with multivariate spatial statistics Open Access

Descriptions

Other title
Subject/Keyword
sequential simulation
spatial variability characterization
conditional probability
geostatistics
bivariate probability matrix
minimum Kullback--Leibler distance
facies modelling
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Li, Yupeng
Supervisor and department
Deutsch, Clayton V. (Civil and Environmental)
Examining committee member and department
Joseph, Timothy G. (Civil and Environmental)
Kyriakidis, Phaedon (Department of Geography at University of California Santa Barbara)
Zhang, Peng (Mathematical and Statistical Sciences)
Boisvert, Jeff (Civil and Environmental)
Askari-Nasab, Hooman (Civil and Environmental)
Department
Department of Civil and Environmental Engineering
Specialization

Date accepted
2011-09-26T22:09:21Z
Graduation date
2011-11
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
This dissertation proposes an improved facies modelling methodology that involves a new geological spatial characterization tool, a geological based spatial distance calculation, and a theoretically sound conditional probability calculation. The full set of bivariate probabilities are proposed as a spatial characterization tool that integrates facies stacking information into the final facies model construction. After inference in the vertical direction from well data, they can be transformed to any spatial distance vector based on a heterogeneity prototype and the calculation approach proposed in this research. The data information carried by the bivariate probabilities will be integrated together into a multivariate probability based on the minimum Kullback--Leibler distance. From this estimated multivariate probability, the conditional probability for each unsampled location is calculated directly. The research developed in this dissertation adds a new geostatistical facies modelling approach to currently available tools. It provides a new approach to integrate more geological understanding in the final model. It could be used in practice and as a seed for further development.
Language
English
DOI
doi:10.7939/R3FW98
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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