This decommissioned ERA site remains active temporarily to support our final migration steps to https://ualberta.scholaris.ca, ERA's new home. All new collections and items, including Spring 2025 theses, are at that site. For assistance, please contact erahelp@ualberta.ca.
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
- 1Artificial Neural Network (ANN)
- 1Data-Driven Modeling
- 1Heterogeneities Characterization
- 1Lean Zone
- 1Production Analysis
- 1Reservoir Heterogeneities
Results for "Probability Distributions on a Circle"
-
Practical Integration of Data-Driven Models for Production Analysis and Inference of Reservoir Heterogeneities in SAGD Operations
DownloadSpring 2018
these trained models are shown to be both reliable and satisfactory. Next, a series of synthetic SAGD models based on typical Athabasca oil reservoir properties and operating conditions is constructed. Heterogeneities are modeled by randomly sampling distribution, volume, and orientation of shale
barriers and lean zones from several probability distributions inferred from field data, and are superposed to the base homogenous models. Many parameterization schemes are investigated to extract input and output parameters from production time-series data and heterogeneous configurations, respectively
is of great interest to propose a feasible SAGD analysis alternative that is capable of utilizing these field data for production analysis and heterogeneities characterization. Data-driven modeling techniques, which involve data analytics and implementation of artificial intelligence (AI) methods for