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Improved facies modelling with multivariate spatial statistics Open Access


Other title
sequential simulation
spatial variability characterization
conditional probability
bivariate probability matrix
minimum Kullback--Leibler distance
facies modelling
Type of item
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 of Civil and Environmental Engineering

Date accepted
Graduation date
Doctor of Philosophy
Degree level
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.
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