--> Abstract: Thin-Bed Reflectivity Inversion and Seismic Interpretation; #90063 (2007)

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Thin-Bed Reflectivity Inversion and Seismic Interpretation

 

Chopra, Satinder1, John P. Castagna2 (1) Arcis Corporation, Calgary, AB (2) Fusion Petroleum Technologies/University of Houston, Houston, TX

 

Enhancing the bandwidth of surface seismic data has always been a desirable goal for geoscientists. Conventional wisdom dictates that in the presence of noise and consequent broadening of the seismic wavelet during its subsurface journey, the resolution limit is a quarter of the dominant wavelength of the data. This limit follows from the Widess model, which is essentially a special case of a realistic model. Based on an analytical analysis of a realistic model, it is found that the seismic amplitude and frequency vary continuously far below the conventional view of the limit of seismic resolution and it is possible to infer thickness below the seismic sample rate. This implies that frequency beyond the seismic data bandwidth can be recovered.

 

Thin-bed spectral inversion method is a novel way of removing wavelet from the seismic data and extracting reflectivity. This inversion process does not require stringent assumptions for its performance. It does not require any a priori model, any reflectivity assumptions or horizon constraints and neither is a well constraint mandatory, though having at one well control point is helpful. For data with high signal-to-noise ratio, thicknesses far below tuning can be resolved. Appreciable noise in the data deteriorates the performance of the inversion outside the frequency band of the original seismic data, but the method still enhances high frequencies within the band without blowing up noise as conventional deconvolution would do. Nevertheless, the highly resolved seismic data retrieved in the form of reflectivity data is very useful for making accurate interpretations and proves to be advantageous in many ways.

 

AAPG Search and Discover Article #90063©2007 AAPG Annual Convention, Long Beach, California