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Abstract
Shrestha, R. K., S. Faruqi, M. Boeckman, and M.-K. Wang, 
inversion
and geostatistical reservoir modeling of the Rotliegend gas field, 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:10.1306/1063819CA53238
2006 by The American Association of Petroleum Geologists.
Three-dimensional Seismic Inversion and Geostatistical Reservoir Modeling of the Rotliegend Gas Field
Inversion and Geostatistical Reservoir Modeling of the Rotliegend Gas Field
R. K. Shrestha,1 M. Boeckmann,2 S. Faruqi,3 M.-K. Wang4
1Halliburton Digital and Consulting Solutions Houston, U.S.A. (formerly Veritas Digicon, Inc.); present address: ConocoPhillips Company, Houston, Texas, U.S.A.
2RWE-Dea AG Exploration and Production Hamburg, Germany
3Occidental Oil and Gas Corporation Houston, U.S.A. (formerly Veritas Digicon, Inc.)
4Eni Petroleum Houston, U.S.A. (formerly Veritas Digicon, Inc.)
ACKNOWLEDGMENTS
The authors gratefully appreciate BEB Erdgas und Erdoel GmbH, Mobil Erdoel-Erdgas GmbH, RWE-DEA AG, and Wintershall AG Erdoelwerke, Hamburg, Germany, for their permission to publish this study. Thanks are due to William M. Bashore, Udo G. Araktingi, Tien-when Lo, Brad Bankhead, and Sean Carey for reviewing this chapter and providing valuable comments.
ABSTRACT
This chapter describes a study of the Rotliegend gas field in Germany in which impedance-constrained facies and property models were constructed to reevaluate the field and guide future development.
Three-dimensional (3-D) stochastic seismic inversion produces a high-resolution acoustic impedance volume by integrating well data with fine vertical sampling and seismic data with dense areal sampling. This acoustic impedance volume can be used as soft data in geostatistical reservoir property modeling. The dense areal sampling in the 3-D seismic grid provides valuable information in interwell areas. Impedance data are particularly useful where well data
are sparse.
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