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Theses and Dissertations
This collection contains theses and dissertations of graduate students of the University of Alberta. The collection contains a very large number of theses electronically available that were granted from 1947 to 2009, 90% of theses granted from 2009-2014, and 100% of theses granted from April 2014 to the present (as long as the theses are not under temporary embargo by agreement with the Faculty of Graduate and Postdoctoral Studies). IMPORTANT NOTE: To conduct a comprehensive search of all UofA theses granted and in University of Alberta Libraries collections, search the library catalogue at www.library.ualberta.ca - you may search by Author, Title, Keyword, or search by Department.
To retrieve all theses and dissertations associated with a specific department from the library catalogue, choose 'Advanced' and keyword search "university of alberta dept of english" OR "university of alberta department of english" (for example). Past graduates who wish to have their thesis or dissertation added to this collection can contact us at erahelp@ualberta.ca.
Items in this Collection
- 2SAGD
- 1APDS
- 1Approximate Physics Discrete Simulation
- 1Chance Constrained Model Predictive Control
- 1Geological Uncertainty
- 1Reservoir Management
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Spring 2017
and parameter uncertainties. Robust optimization (RO) approximation, a novel method dealing with joint chance constraints, is investigated to solve CCMPC problem. This method leads to results close to the true optimal and is not restricted to certain types of distribution. This work is further applied
on the steam assisted gravity drainage (SAGD) process. Constraint violations are greatly reduced by using the RO method. For system noises, the RO method can be directly applied with the inclusion of uncertainty sets. The type of uncertainty set is selected based on the distribution. Two-layer
optimization is proposed, one layer guarantees probability satisfaction and the other layer deals with optimizing the cost. Compared with traditional analytical methods, RO method is not limited to specific distribution and shows better performance in objective function. For parameter uncertainties, random
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Spring 2019
. This requires transferring the geological uncertainty to probability distributions of a response variable suitable for decision-making and use of a decision criterion that considers the reservoir manager’s preferences toward the project’s return-risk trade-off. This is challenging in petroleum
transfer concepts and ideas from discrete simulation. It works on homogeneous and heterogeneous reservoirs and is computationally efficient enough to be applied over multiple geostatistical realizations. A case study performed with a realistic multi-realization geological model validates the predictive
making under geological uncertainty. MVC-SDR does not rely on a specific utility function and leads to decisions that are considered rational to risk-averse reservoir managers. The shortcoming is a reduced ability to rank projects with very similar value. Two examples illustrate the use of MVC-SDR, the