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The AAPG/Datapages Combined Publications Database
AAPG Bulletin
Abstract
AAPG Bulletin, V.
1Manuscript received June 9, 1997;
revised manuscript received July 20, 1998; final acceptance February 19,
1999.
2Stanford Center for Reservoir Forecasting,
Department of Petroleum Engineering, Stanford University, Stanford, California
94305-2220. Present address: Institute of Hydrogeology and Geothermics,
Joanneum Research Lim., Elisabethstr. 16, A-8010 Graz, Austria; e-mail:
hans.kupfersberger@ joanneum.ac.at
3Stanford Center for Reservoir Forecasting,
Department of Petroleum Engineering, Stanford University, Stanford, California
94305-2220. Present address: Department of Civil and Environmental Engineering,
University of Alberta, Edmonton, Alberta T6G 2G7, Canada.
Kupfersberger has been jointly funded by the
Stanford Center for Reservoir Forecasting and the Fonds zur Förderung
der Österreichischen Forschung, project number J01059-GEO. We thank
J. Yarus and R. Tillman for their constructive comments during the review
process of this paper.
ABSTRACT
Permeability estimation in three dimensions commonly
suffers from inadequate horizontal data sampling. Thus, modeling the variogram
in the horizontal plane and determining anisotropy
ratios in the vertical
direction is associated with substantial uncertainty. This uncertainty
about spatial correlation of permeability is transferred into uncertainty
in reservoir performance forecasting. Analog information, such as data
borrowed from more extensively sampled fields or horizontal correlation
measures derived from geologic process modeling, may be used to narrow
this uncertainty. We present a compilation of published results of horizontal
and vertical variograms of petrophysical parameters. A stepwise procedure
describing a process to establish horizontal variograms for numerical geological
modeling is developed based on the distinction between three different
types of
anisotropy
. We also emphasize the challenge of meaningful combining
data of varying volume support and the need for modeling decisions.
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