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Permanent link (DOI): https://doi.org/10.7939/R3QF8JS95

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Data-Driven Proxy Modeling during SAGD Operations in Heterogeneous Reservoirs Open Access

Descriptions

Other title
Subject/Keyword
ANN
Heterogeneous Reservoirs
Proxy Modeling
SAGD
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Amirian, Ehsan
Supervisor and department
Leung, Juliana (Department of Civil and Environmental Engineering)
Examining committee member and department
Fayek, Aminah R (Department of Civil and Environmental Engineering)
Li, Huazhou (Department of Civil and Environmental Engineering)
Department
Department of Civil and Environmental Engineering
Specialization
Petroleum Engineering
Date accepted
2014-01-29T11:53:35Z
Graduation date
2014-06
Degree
Master of Science
Degree level
Master's
Abstract
Evaluation of steam-assisted gravity drainage (SAGD) performance that involves detailed compositional simulations is usually deterministic, cumbersome, expensive (manpower and time consuming), and not quite suitable for practical decision making and forecasting, particularly when dealing with high-dimensional data space consisting of large number of operational and geological parameters. Data-driven modeling techniques, which entail comprehensive data analysis and implementation of machine learning methods for system forecast, provide an attractive alternative. In this thesis, Artificial Neural Network (ANN) is employed as a data-driven modeling alternative to predict SAGD production in heterogeneous reservoirs. Numerical flow simulations are performed to construct a training data set consists of various attributes describing characteristics associated with reservoir heterogeneities and other relevant operating parameters. Finally, several case studies are studied to demonstrate the improvements in robustness and accuracy of the prediction when cluster analysis techniques are performed to identify internal data structures and groupings prior to ANN modeling.
Language
English
DOI
doi:10.7939/R3QF8JS95
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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