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Recent Advances in Relative Amplitude 3D Seismic Data Processing
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, 3rd International Congress of the Brazilian Geophysical Society, Nov 1993, cp-324-00062
Abstract
The amplitude of seismic reflections contains valuable information about the subsurface of the earth and careful processing of the data Is essential to preserve it. During the last few years there has been important progress in the acquisition and processing of seismic data to increase the reliability of seismic amplitude. Relative amplitude processing is adding valuable information to the understanding of hydrocarbon reservoirs, complementing traveltime Information, and leading to a unified, more consistent geologic Interpretation when integrated to other non-seismic information. Our paper discusses three significant new developments in relative amplitude processing of 3-D seismic data: surface- and subsurfaceconsistent amplitude processing in the presence of noise, dynamic amplitude decomposition of transmission effects, and dip moveout (DMO) equalization. Together they increase the quality of the seismic 3-D image and provide more reliable amplitude information for interpretation. Surface- and subsurface-consistent amplitude processing identifies and corrects for the. variability introduced by the instrumentation and for near surface distortions. Dynamic amplitude decomposition identifies and compensates distortions introduced by the cumulative effects of wave propagation, in particular transmission losses in the overburden. DMO is a powerful technique that has become standard during seismic data processing. DMO maps the energy to zero source-to-receiver distance, decreasing the dependence of traveltimes on offset, therefore improving the quality of the seismic stacked section. An important aspect of DMO that is often of crucial importance in everyday seismic data processing is the effect of sparse or irregular spatial sampling on DMO-processed data. I DMO equalization is an efficient process based on the decomposition of DMO ihto its constituent dip components that accounts for the effects of irregular spatial sampling for both flat and dipping events, minimizing amplitude distortions related to the acquisition geometry.