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Geostatistics for Naturally Fractured Reservoirs Open Access


Other title
discrete fracture network
naturally fractured reservoir
Type of item
Degree grantor
University of Alberta
Author or creator
Niven, Eric B
Supervisor and department
Deutsch, Clayton (Mining Engineering)
Examining committee member and department
Boisvert, Jeff (School of Mining and Petroleum Engineering)
Joeseph, Tim (School of Mining and Petroleum Engineering)
Jones, Brian (Earth and Atmospheric Sciences)
Deutsch, Clayton (Mining Engineering)
Aguilera, Roberto (Chemical and Petroleum Engineering, University of Calgary)
Nouri, Alireza (School of Mining and Petroleum Engineering)
Department of Civil and Environmental Engineering
Mining Engineering
Date accepted
Graduation date
Doctor of Philosophy
Degree level
A common problem in naturally fractured reservoirs (NFRs) is a lack of data caused by few wells; or at least, few wells with core or borehole images. Secondary data (such as seismic) can be used to improve predictions of fracture intensity in between the wells. Common geostatistical techniques for incorporating secondary data rely heavily on the correlation coefficient, which is influenced by outliers and whose uncertainty is usually unknown or not assessed in practice. A novel method is developed for calculating a robust correlation coefficient and propagating uncertainty in the correlation through reservoir modelling of fracture intensity. Discrete fracture networks (DFNs) are created to reproduce the models of fracture intensity. Current DFN modelling techniques incorporate and honour some geological information such as intensity and orientation data. However, most DFN modelling algorithms and software do not account for similarity in the orientation of nearby fractures, fracture network connectivity or fracture spacing in an explicit manner. This thesis shows that some natural fracture networks are not realistically modelled by conventional techniques. A new discrete fracture network simulation algorithm is developed, which works by simulating more fractures than are required and iterating to find a subset that best matches target spatial statistics. It is shown that the proposed simulation algorithm results in fracture networks that are more geologically realistic compared with the traditional methods. The increase in geological realism is expected to lead to better resource predictions and economic decisions for reservoir management.
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.
Citation for previous publication
Niven, Eric B. and Deutsch, Clayton V. (2012). Calculating a robust correlation coefficient and quantifying its uncertainty, Computers & Geosciences, Volume 40, Pages 1-9.

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