Abstract
Reproduction of complex 3D patterns is not possible using algorithms that are constrained to two-point (covariance or variogram) statistics. A unique pattern-growth algorithm (GrowthSim) is presented in this paper that performs multiple point spatial simulation of patterns conditioned to multiple point data. Starting from conditioning data locations, patterns are grown constrained to the pattern statistics inferred from a training image. This is in contrast to traditional multiple-point statistics based-algorithms where the simulation progresses one node at a time. In order to render this pattern growth algorithm computationally efficient, two strategies are employed—(i) computation of an optimal spatial template for pattern retrieval, and (ii) pattern classification using filters. To accurately represent the spatial continuity of large-scale features, a multi-level simulation scheme is implemented. In addition, a scheme for applying affine transformation to spatial patterns is presented to account for local variation in spatial patterns in a target reservoir. The GrowthSim algorithm is demonstrated for developing the reservoir model for a deepwater turbidite system. Lobes and channels that exhibit spatial variations in orientation, density and meandering characteristics characterize the reservoir. The capability of GrowthSim to represent such non-stationary features is demonstrated.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Arpat G, Caers j (2004) A multiple-scale, pattern-based approach to sequential simulation. In: Proceedings of the 7th international geostatistics congress, GEOSTAT 2004, Banff, Canada, October 2004
Coleman DA, Woodruff DL (2000) Cluster analysis for large datasets: an effective algorithm for maximizing the mixture likelihood. J Comput Graph Stat 9(4):672–688
Deutsch C, Journel A (1998) GSLIB: geostatistical software library and user’s guide. Oxford University Press, London
Isaaks E (1990) The application of Monte Carlo methods to the analysis of spatially correlated data. PhD thesis, Stanford University
Strebelle S (2002) Conditional simulation of complex geological structures using multiplepoint statistics. Math Geol 34(1):1–21
Strebelle S, Zhang T (2005) Non-stationary multiple-point geostatistical models. In: Geostatistics Banff 2004, vol 1. Quantitative Geology and Geostatistics, vol 14, pp 235–244. doi:10.1007/978-1-4020-3610-124
Zhang T (2006) Filter-based training pattern classification for spatial pattern simulation. PhD thesis, Stanford University, Stanford, CA
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Huang, YC., Srinivasan, S. (2012). Efficient Conditional Simulation of Spatial Patterns Using a Pattern-Growth Algorithm. In: Abrahamsen, P., Hauge, R., Kolbjørnsen, O. (eds) Geostatistics Oslo 2012. Quantitative Geology and Geostatistics, vol 17. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4153-9_17
Download citation
DOI: https://doi.org/10.1007/978-94-007-4153-9_17
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-4152-2
Online ISBN: 978-94-007-4153-9
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)