CRS Prestack Data Conditioning for Extending AVO Analysis to Low-fold and Low-quality Data Zones

Abstract

The Common-Reflection-Surface (CRS) method may improve seismic processing beyond imaging, e.g. in an enhanced Amplitude Versus Offset (AVO) analysis. Various applications have shown that the more realistic subsurface assumptions, and the increased fold of the CRS imaging allow to extend AVO analysis into noise zones and to deep targets with low signal quality. Extreme fluctuations of AVO parameters are removed, and AVO anomalies are enhanced. The CRS method assumes subsurface reflector elements with dip and curvature, which implies large-fold stacking surfaces extending both across offset, and across neighboring CMP locations. The extension across neighboring CMPs defines a CRS gather at the central CMP location, comprising data from a multitude of traces. The CRS moveout correction compensates for the local dip across these neighboring CMPs, thus contrasting to conventional AVO super-gathers based on NMO correction that collect dipping events horizontally at varying phase. The presented case studies show that CRS-AVO attribute stacks are produced with a much higher signal-to-noise ratio from CRS gathers than from CMP gathers in conventional AVO. The CRS-AVO attribute sections clearly distinguish anomalies at known or expected gas-bearing reservoirs.