Gleaning Meaningful Information From Seismic Attributes

Seismic attributes form an integral part of most of today’s interpretation projects. Attributes enhance subtle features in the seismic data that may otherwise be overlooked or require a great deal of time to map. The quality of attribute displays is directly proportional to the quality of the input seismic data. Ideally, all amplitude, phase, and travel time distortion effects due to near-surface and overburden heterogeneities as well as those introduced by acquisition and processing should be optimally handled, even if they cannot be totally eliminated. In practice, even with careful acquisition, processing, and imaging, our data will still exhibit a certain level of noise. We show how structure-oriented filtering can eliminate random noise, with the principal component filter providing better results than the more familiar mean and median filters. The acquisition footprint is a form of coherent, rather than random, noise and requires a different filtering approach, ideally in the prestack domain prior to stacking. Finally, we show that different implementations of a given attribute can make a difference, with energy ratio coherence providing more robust images than semblance. We illustrate these findings through application to a suite of examples from Alberta, Canada.

[1]  Yi Luo,et al.  3D edge-preserving smoothing and applications , 2002 .

[2]  P. Thierry.  Attenuation of Acquisition Footprint For Non-orthogonal 3D Geometries , 2002 .

[3]  Kurt J. Marfurt,et al.  Robust estimates of 3D reflector dip and azimuth , 2006 .

[4]  Kurt J. Marfurt,et al.  Eigenstructure-based coherence computations as an aid to 3-D structural and stratigraphic mapping , 1999 .

[5]  Michael Jervis,et al.  Edge Preserving Filtering On 3-D Seismic Data Using Complex Wavelet Transforms , 2006 .

[6]  Kurt J. Marfurt,et al.  Seismic Attributes for Prospect Identification and Reservoir Characterization , 2007 .

[7]  William J. Done.  Removal of Interference Patterns in Seismic Gathers , 2009 .

[8]  James W. Ryan,et al.  Adapting to Noisy 3D Data - Attenuating the Acquisition Footprint. , 2000 .

[9]  T. Randen,et al.  Three-Dimensional Texture Attributes For Seismic Data Analysis , 2000 .

[10]  A. Frank Linville,et al.  A procedure for optimally removing localized coherent noise , 1995 .

[11]  Panos G. Kelamis,et al.  Acquisition footprint suppression via the truncated SVD technique: Case studies from Saudi Arabia , 2005 .

[12]  Milos Cvetkovic,et al.  2D Stationary Wavelet Transform Based Acquisition Footprint Suppression , 2007 .

[13]  Shuki Ronen,et al.  Footprint Analysis of Land and TZ Acquisition Geometries Using Synthetic Data , 1999 .

[14]  David J. Monk,et al.  Prediction Filtering For 3-D Poststack Data , 1993 .

[15]  Gijs C. Fehmers,et al.  Fast structural interpretation with structure-oriented filtering , 2002 .

[16]  Kurt J. Marfurt,et al.  Eigenstructure Based Coherence Computations , 1996 .

[17]  J. Marfurt.  obust estimates of 3 D reflector dip and azimuth urt , 2006 .

[18]  Sergey Fomel,et al.  Applications of plane-wave destruction filters , 2002 .

[19]  R. Lynn Kirlin,et al.  3-D seismic attributes using a semblance‐based coherency algorithm , 1998 .

[20]  Christopher D. Walker,et al.  Marine 3D Geometry Design for Optimum Acquisition System Response , 1995 .

[21]  Kurt J. Marfurt,et al.  Coherency calculations in the presence of structural dip , 1999 .

[22]  Kurt J. Marfurt,et al.  Suppression of the acquisition footprint for seismic sequence attribute mapping , 1998 .

[23]  W. G. Higgs,et al.  Edge detection and stratigraphic analysis using 3D seismic data , 1996 .