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Permeability estimation of fracture networks Open Access


Other title
Petroleum engineering
Artificial neural network
Percolation theory
Fracture network
Type of item
Degree grantor
University of Alberta
Author or creator
Jafari, Alireza
Supervisor and department
Babadagli, Tayfun (Department of Civil and Environmental Engineering, University of Alberta)
Examining committee member and department
Safouhi, Hassan (Campus Sainte Jean, University of Alberta)
Babadagli, Tayfun (Department of Civil and Environmental Engineering, University of Alberta)
Al-Hossein, Mohamed (Department of Civil and Environmental Engineering, University of Alberta)
Kuru, Ergun (Department of Civil and Environmental Engineering, University of Alberta)
Asghari, Koorosh (Petroleum Systems Engineering, University of Regina)
Department of Civil and Environmental Engineering

Date accepted
Graduation date
Doctor of Philosophy
Degree level
This dissertation aims to propose a new and practical method to obtain equivalent fracture network permeability (EFNP), which represents and replaces all the existing fractures located in each grid block for the reservoir simulation of naturally fractured reservoirs. To achieve this, first the relationship between different geometrical properties of fracture networks and their EFNP was studied. A MATLAB program was written to generate many different realizations of 2-D fracture networks by changing fracture length, density and also orientation. Next, twelve different 2-D fractal-statistical properties of the generated fracture networks were measured to quantify different characteristics. In addition to the 2-D fractal-statistical properties, readily available 1-D and 3-D data were also measured for the models showing variations of fracture properties in the Z-direction. The actual EFNP of each fracture network was then measured using commercial software called FRACA. The relationship between the 1-, 2- and 3-D data and EFNP was analyzed using multivariable regression analysis and based on these analyses, correlations with different number of variables were proposed to estimate EFNP. To improve the accuracy of the predicted EFNP values, an artificial neural network with the back-propagation algorithm was also developed. Then, using the experimental design technique, the impact of each fracture network parameter including fracture length, density, orientation and conductivity on EFNP was investigated. On the basis of the results and the analyses, the conditions to obtain EFNP for practical applications based on the available data (1-D well, 2-D outcrop, and 3-D welltest) were presented. This methodology was repeated for natural fracture patterns obtained mostly from the outcrops of different geothermal reservoirs. The validity of the equations was also tested against the real welltest data obtained from the fields. Finally, the concept of the percolation theory was used to determine whether each fracture network in the domain is percolating (permeable) and to quantify the fracture connectivity, which controls the EFNP. For each randomly generated fracture network, the relationship between the combined fractal-percolation properties and the EFNP values was investigated and correlations for predicting the EFNP were proposed. As before, the results were validated with a new set of fracture networks.
License granted by Alireza Jafari ( on 2010-11-10T05:16:34Z (GMT): 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 the above terms. The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein 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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