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The AAPG/Datapages Combined Publications Database
AAPG Special Volumes
Abstract
Pyrcz, M. J.,E. Gringarten, P. Frykman, and C. V. Deutsch,
simulation
, in T. C. Coburn, J. M. Yarus, and R. L. Chambers, eds., Stochastic
modeling
and geostatistics: Principles, methods, and case studies, volume II: AAPG Computer Applications in Geology 5, p.
DOI:10.1306/1063811CA53230
Representative Input Parameters for Geostatistical
Simulation
Simulation
M. J. Pyrcz,1 E. Gringarten,2 P. Frykman,3 C. V. Deutsch4
1Chevron Energy Technology Company Houston, Texas, U.S.A.
2Earth Decision Houston, Texas, U.S.A.
3Geological Survey of Denmark and Greenland Copenhagen, Denmark
4University of Alberta Edmonton, Alberta, Canada
ABSTRACT
Geostatistical-
simulation
techniques are increasingly being used to create heterogeneous realizations for flow
modeling
and to assess uncertainty in hydrocarbon resources and reserves. These geostatistical-
simulation
techniques reproduce the input statistics within ergodic fluctuations. The input statistics representing various model parameters must be computed from data that are representative of the entire domain being modeled. Geostatistical
simulation
does not accommodate a lack of representativeness in the data. Moreover, the extent to which the input statistics are reproduced depends almost exclusively on the size of the
modeling
domain relative to the range of spatial correlation; fluctuations in realizations of the full
reservoir
model do not depend entirely on the uncertainty of the input statistics. It is necessary to explicitly incorporate the uncertainty of the input statistics because they have a much larger and more realistic impact on the uncertainty of the full
reservoir
model than stochastic fluctuations. The best practices for determining representative input values of model parameters and quantification of their uncertainty are presented in this chapter.
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