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Geostatistical Reservoir Modeling with Parameter Uncertainty in Presence of Limited Well Data Open Access


Other title
Histogram Uncertainty
Variogram Uncertainty
Variogram Declustering
Fourth Order Covariance
Type of item
Degree grantor
University of Alberta
Author or creator
Rezvandehy, Mehdi
Supervisor and department
Clayton V. Deutsch (Civil and Environmental Engineering)
Examining committee member and department
Deepak Devegowda (Petroleum and Geological Engineering)
Japan Trivedi (Civil and Environmental Engineering)
Juliana Leung (Civil and Environmental Engineering)
Jeffery Boisvert (Civil and Environmental Engineering)
Department of Civil and Environmental Engineering
Mining Engineering
Date accepted
Graduation date
2017-06:Spring 2017
Doctor of Philosophy
Degree level
An important challenge in reservoir management is establishing reliable numerical geological models of all required flow parameters including facies, porosity and permeability. These numerical models are driven by conceptual geology, seismic, production and well data that are widely spaced early in exploration. Critical decisions are made early in the reservoir lifecycle where limited seismic and production data may be available. Geostatistical simulation is commonly used to construct these numerical models and quantify uncertainty. This thesis develops techniques that improve the uncertainty represented in the final geostatistical model. The variogram is a key parameter for geostatistical simulation. In presence of preferential positioning of the wells to maximize production, variogram modeling is suboptimal. A novel technique is proposed to weight variogram pairs in order to compensate for preferential or clustered sampling. Weighting the variogram pairs helps remove noise and minimize artifacts in the experimental variogram. A new approach of variogram uncertainty is developed since variogram declustering does not remove all uncertainty in the experimental variogram. Variogram realizations are drawn from the uncertainty interval of lag distances honoring the correlation between lags. The realizations are transferred to geostatistical simulation to incorporate variogram uncertainty in the numerical geological models. A methodology to improve horizontal variogram inference from the widely spaced well data is developed considering seismic data and the vertical well variogram. Seismic data provide constraints on the unknown horizontal variogram of the well data. The vertical variogram of the well data can be scaled to scenarios of the horizontal variogram. Improved horizontal variogram realizations are achieved by considering these constraints. Uncertainty in the histogram of flow parameters affects resource/reserve estimation, investment and development decisions. A new simulation-based approach of quantifying histogram uncertainty is also established. A methodology to calculate the true histogram uncertainty for a single variable in certain circumstances is proposed. This allows checking the proposed spatial bootstrap methodology. Multivariate distribution uncertainty is implemented considering the correlation between variables. The applicability of the proposed methodology for variogram and histogram uncertainties are shown with an offshore real reservoir located in the Dutch sector of the North Sea. This case study confirms that the histogram uncertainty has the highest impact on resource estimation.
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.
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