AAPG Bulletin, V. 83 (1999), No. 8 (August 1999), P. 1262-1278.

Methodology for Integrating Analog Geologic Data in 3-D Variogram Modeling¹

H. Kupfersberger² and C. V. Deutsch³

©Copyright 1999.  The American Association of Petroleum Geologists.  All Rights Reserved

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.