Representative Input Parameters for Geostatistical Simulation
M. J. Pyrcz, E. Gringarten, P. Frykman, C. V. Deutsch, 2006. "Representative Input Parameters for Geostatistical Simulation", Stochastic Modeling and Geostatistics: Principles, Methods, and Case Studies, Volume II, T. C. Coburn, J. M. Yarus, R. L. Chambers
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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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Since publication of the first volume of Stochastic Modeling and Geostatistics in 1994, there has been an explosion of interest and activity in geostatistical methods and spatial stochastic modeling techniques. Many of the computational algorithms and methodological approaches that were available then have greatly matured, and new, even better ones have come to the forefront. Advances in computing and increased focus on software commercialization have resulted in improved access to, and usability of, the available tools and techniques. Against this backdrop, Stochastic Modeling and Geostatistics Volume II provides a much-needed update on this important technology. As in the case of the first volume, it largely focuses on applications and case studies from the petroleum and related fields, but it also contains an appropriate mix of the theory and methods developed throughout the past decade. Geologists, petroleum engineers, and other individuals working in the earth and environmental sciences will find Stochastic Modeling and Geostatistics Volume II to be an important addition to their technical information resources.