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Multivariate Analysis of Diverse Data for Improved Geostatistical Reservoir Modeling Open Access


Other title
kernel density estimation
marginal fitting
data integration
Type of item
Degree grantor
University of Alberta
Author or creator
Hong, Sahyun
Supervisor and department
Deutsch, Clayton (civil and environmental engineering)
Examining committee member and department
Jensen, Jerry (petroelum engineering, univ of Calgary)
Askari-Nasab, Hooman(civil and environmental engineering)
Szymanski, Jozef(civil and environmental engineering)
Hooper, Peter (Statistical science)
Department of Civil and Environmental Engineering

Date accepted
Graduation date
Doctor of Philosophy
Degree level
Improved numerical reservoir models are constructed when all available diverse data sources are accounted for to the maximum extent possible. Integrating various diverse data is not a simple problem because data show different precision and relevance to the primary variables being modeled, nonlinear relations and different qualities. Previous approaches rely on a strong Gaussian assumption or the combination of the source-specific probabilities that are individually calibrated from each data source. This dissertation develops different approaches to integrate diverse earth science data. First approach is based on combining probability. Each of diverse data is calibrated to generate individual conditional probabilities, and they are combined by a combination model. Some existing models are reviewed and a combination model is proposed with a new weighting scheme. Weakness of the probability combination schemes (PCS) is addressed. Alternative to the PCS, this dissertation develops a multivariate analysis technique. The method models the multivariate distributions without a parametric distribution assumption and without ad-hoc probability combination procedures. The method accounts for nonlinear features and different types of the data. Once the multivariate distribution is modeled, the marginal distribution constraints are evaluated. A sequential iteration algorithm is proposed for the evaluation. The algorithm compares the extracted marginal distributions from the modeled multivariate distribution with the known marginal distributions and corrects the multivariate distribution. Ultimately, the corrected distribution satisfies all axioms of probability distribution functions as well as the complex features among the given data. The methodology is applied to several applications including: (1) integration of continuous data for a categorical attribute modeling, (2) integration of continuous and a discrete geologic map for categorical attribute modeling, (3) integration of continuous data for a continuous attribute modeling. Results are evaluated based on the defined criteria such as the fairness of the estimated probability or probability distribution and reasonable reproduction of input statistics.
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