Abstract
In the past ten years, conditional simulation has come be used in analysis and mapping of regionalized variables. An important advantage to the geostatistical approach to mapping lies in the modeling of spatial covariance that precedes interpolation; semivariogram models derived from this step can make the final estimates sensitive to directional anisotropies present in the data. On the other hand, the smoothing property of kriging can also mean that one throws away detail at the mapping stage.
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References
Almeida A.S. and Frykman, P. (1994) Geostatistical modeling of chalk reservoir properties in the Dan Field, Danish North Sea, in J.M. Yarus, and R.L. Chambers (eds), Stochastic Modeling and Geostatistics. American Association of Petroleum Geologists, Tulsa, OK, pp. 273–286.
Chambers, R.L., Zinger, M.A. and Kelly, M.C. (1994) Constraining geostatistical reservoir descriptions with 3-D seismic data to reduce uncertainty, in J.M. Yarus and R. Chambers (eds), Stochastic Modeling and Geostatistics. American Association of Petroleum Geologists, Tulsa, OK, pp. 143–157.
Davis, M. (1987) Generating large stochastic simulations — the matrix polynomial approximation method. Math. Geol., 19(2), 99–108.
Deutsch, C.V. (1993) Conditioning reservoir models to well test information, in A. Soares (ed.) Geostatistics Troia ‘92. Kluwer Academic Publishers, Dordrecht, pp. 505–518.
Deutsch, C.V. and Journel, A.G. (1992) GSLIB: Geostatistical Software Library and User’s Guide. Oxford University Press, New York, 340pp.
Dowd, P.A. and Saraç, C. (1994) An extension of the LU decomposition method of simulation, in M. Armstrong and P. A. Dowd (eds) Geostatistical Simulations. Kluwer Academic Publishers, Dordrecht, p. 23–36.
Gotway, C.A. and Rutherford, B.M. (1994) Stochastic simulation for imaging spatial uncertainty: Comparison and evaluation of available algorithms, in M. Armstrong and P.A. Dowd (eds) Geostatistical Simulations. Kluwer Academic Publishers, Dordrecht, pp. 1–21.
Haas, T.J. (1990) Lognormal and moving window methods of estimating acid deposition. Journal of the American Statistical Association, 85, 950–963.
Isaaks, E. (1990) The application of Monte Carlo Methods to the Analysis of Spatially Correlated Data. Unpublished PhD dissertation, Stanford University
Journel, A.G. and Huijbregts, C.J. (1978) Mining Geostatistics. Academic Press, London, 600 pp.
Murray, C.J. (1994) Identification and 3-D modeling of petrophysical rock types, in J.M. Yams and R.L. Chambers (eds) Stochastic Modeling and Geostatistics. American Association of Petroleum Geologists, Tulsa, OK, pp. 323–337.
Verly, G.W. (1993) Sequential Gaussian cosimulation: A simulation method integrating several types of information, in A. Soares (ed.) Geostatistics Trüia ‘82,Vol. 1. Kluwer Academic Publishers, Dordrecht, pp. 543–554.
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© 1999 Springer Science+Business Media Dordrecht
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Hohn, M.E. (1999). Conditional simulation. In: Geostatistics and petroleum geology. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-4425-4_7
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DOI: https://doi.org/10.1007/978-94-011-4425-4_7
Publisher Name: Springer, Dordrecht
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