--> Abstract: Seismic Attributes for Fault/Fracture Characterization, by by Satinder Chopra and Kurt J. Marfurt; #90078 (2008)
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Previous HitSeismicNext Hit Previous HitAttributesNext Hit for Fault/Fracture Characterization

Satinder Chopra1 and Kurt J. Marfurt2
1Arcis Corporation, Calgary, AB, Canada
2University of Oklahoma, Norman, OK

Among the various geophysical techniques available for characterizing faults and fractures, 3D Previous HitseismicNext Hit Previous HitattributesNext Hit are particularly useful for identifying faults, large fractures, or fracture zones. To be useful, Previous HitseismicNext Hit Previous HitattributesNext Hit derived from 3D Previous HitseismicNext Hit volumes need to be sensitive to a desired geologic feature or a reservoir property of interest. This search has led to the development of more and more Previous HitseismicNext Hit Previous HitattributesNext Hit, with the result that there are a few hundred Previous HitseismicNext Hit Previous HitattributesNext Hit that are known, many of which are widely used for lithological and petrophysical prediction of reservoir properties. Of these various Previous HitattributesNext Hit, dip-magnitude, dip-azimuth and coherence Previous HitattributesNext Hit have been used for the detection of faults and fractures. We will also demonstrate how curvature Previous HitattributesNext Hit are specially suited for determining the geometry of such features.

There are many ways to bring out these features of interest in 3D Previous HitseismicNext Hit attribute volumes, some subtle and some prominent. Apart from the choice of the computation parameters, conditioning of the input data, and the choice of attribute algorithms make a significant difference to the quality of the results. In this presentation, we will discuss advantages of volumetric curvature applications in preference to horizon-based curvature. We find that most-positive and most-negative curvature Previous HitattributesNext Hit facilitate a better interpretation of fault/fracture detail than other Previous HitattributesTop. Furthermore, by picking lineaments seen in the most-positive and most-negative curvature displays, we can generate rose diagrams, which when compared with corresponding diagrams from image logs, lend confidence in the performed interpretation.

 

AAPG Search and Discovery Article #90078©2008 AAPG Annual Convention, San Antonio, Texas