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Integration of 4D Seismic Data in Reservoir Characterization with Facies Parameter Uncertainty Open Access


Other title
4D Seismic
Facies Parameter Uncertainty
Type of item
Degree grantor
University of Alberta
Author or creator
Hadavandsiri, Mostafa
Supervisor and department
Dr. Clayton Deutsch (Civil and Environmental Engineering)
Examining committee member and department
Dr. Juliana Leung (Civil and Environmental Engineering)
Dr. Laurence Lines (Geo-science, University of Calgary)
Dr. Yashar Pourrahimian (Civil and Environmental Engineering)
Dr. Jeffery Boisvert (Civil and Environmental Engineering)
Dr. Alireza Nouri (Civil and Environmental Engineering)
Department of Civil and Environmental Engineering
Mining Engineering
Date accepted
Graduation date
2017-11:Fall 2017
Doctor of Philosophy
Degree level
Reservoir exploration and production are always conducted in presence of geological uncertainty that is an inevitable result of incomplete data and heterogeneity at all scales. Modeling subsurface geology based on limited data is subject to uncertainty and its accurate assessment plays a key role in resource estimation and reservoir management decision making. Canadian oil sand reservoirs are the third largest oil reserves in the world and play a key role in the economy of Canada. There are many challenges and technical details associated with the enhanced oil recovery technologies that are required to produce high-viscosity oil. This increases the importance of an accurate model of geological uncertainty as a necessary input for the exploration planning and reservoir management. An accurate assessment of geological uncertainty requires the modeling workflow to consider (1) all available sources of data to be reproduced and (2) model parameter uncertainty to be included. The geological uncertainty is then represented by multiple geostatistical realizations that can be used simultaneously for optimal reservoir management decision making. In this thesis, a practical framework is developed to improve the model of geological uncertainty. A realistic model of geological uncertainty requires parameter uncertainty associated with the input statistical parameters to be considered. Limited well data does not permit unambiguous specification of the required parameters. These parameters often have a global and widespread influence on the resources and reserves. One of the main contributions of this research is to quantify prior proportion uncertainty for categorical variables such as facies in presence of a trend. The trend model provides additional information about the subsurface geological setting. Facies modeling is of great significance for reservoir characterization as it explains a major aspect of spatial heterogeneity and geological uncertainty. Large-scale flow patterns are often controlled by the spatial arrangement and continuity of facies because, the variability of permeability in between facies is more significant compared to that within facies. Each source of data provides information about the reservoir with different scales and levels of precision. Although there are well-established geostatistical techniques for stochastic simulation of the reservoir conditioned to static data, practical integration of information obtained from dynamic data remains a major challenge. The changes in reservoir properties including fluid saturation, pressure and temperature can be monitored by dynamic data to obtain information about the large scale connectivity and quality of fluid flow within the reservoir. A novel methodology is proposed for effective integration of dynamic data into the geological modeling workflow. This methodology is based on geostatistical enforcement of anomalies identified from dynamic sources of data such as 4D seismic. All geostatistical realizations are updated to honor the information obtained from the dynamic data that become available during the reservoir life cycle.
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
Citation for previous publication
Hadavand, M. and Deutsch , C. V. (2017). Facies Proportion Uncertainty in Presence of a Trend, published by Journal of Petroleum Science and Engineering.Hadavand, M. and Deutsch , C. V. (2016). A Practical Methodology for Integration of 4D seismic in Steam Assisted Gravity Drainage Reservoir Characterization, published by SPE Journal of Reservoir Evaluation and Engineering.

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