Ray-based stochastic inversion of prestack seismic data for improved reservoir characterization
Author(s) -
Dennis van der Burg,
Arie Verdel,
Kees Wapenaar
Publication year - 2009
Publication title -
geophysics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.178
H-Index - 172
eISSN - 1942-2156
pISSN - 0016-8033
DOI - 10.1190/1.3190131
Subject(s) - inversion (geology) , prestack , geology , reservoir modeling , synthetic data , seismic inversion , wavelet , amplitude , layering , seismic trace , offset (computer science) , algorithm , geodesy , seismology , computer science , geometry , mathematics , optics , geotechnical engineering , tectonics , physics , botany , azimuth , artificial intelligence , biology , programming language
Trace inversion for reservoir parameters is affected by angle averaging of seismic data and wavelet distortion on the migration image. In an alternative approach to stochastic trace inversion, the data are inverted prestack before migration using 3D dynamic ray tracing. This choice makes it possible to interweave trace inversion with Kirchhoff migration. The new method, called ray-based stochastic inversion, is a generalization of current amplitude versus offset/amplitude versus angle (AVO/AVA) inversion techniques. The new method outperforms standard stochastic inversion techniques in cases of reservoir parameter estimation in a structurally complex subsurface with substantial lateral velocity variations and significant reflector dips. A simplification of the method inverts the normal-incidence response from reservoirs with approximately planar layering at the subsurface target locations selected for inversion. It operates along raypaths perpendicular to the reflectors, the direction that offers optimal resolution to discern layering in a reservoir. In a test on field data from the Gulf of Mexico, reservoir parameter estimates obtained with the simplified method, the estimates found by conventional stochastic inversion, and the actual values at a well drilled after the inversion are compared. Although the new method uses only 2% of the prestack data, the result indicates it improves accuracy on the dipping part of the reservoir, where conventional stochastic inversion suffers from wavelet stretch caused by migration
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