Amplitude analysis with an optimal model-based linear AVO approximation: Part II — Field data example

Abstract

AVO analysis can be conducted by estimating amplitude variation with offset (AVO) attributes from seismic prestack data and by characterizing the measured amplitude responses by the position of their projection in the attribute space. The most common AVO attributes are the intercept (zero-offset reflectivity) and AVO gradient. We have constructed an optimized, model-based linear AVO equation that is more accurate than usual AVO approximations. The parameters of this equation represent new AVO attributes that are more directly related to the information contained in seismic reflection amplitudes. We use these new AVO attributes to classify reflector responses from field data. Five seismic facies are defined that are characterized by differentdistributions of seismic parameters. Nine reflector classes are formed by associating appropriate pairs of facies. The expected locations of the different reflector classes in the space of optimized attributes are found by modeling and are used to derive a classification scheme. This scheme is applied to sections of optimized attributes calculated from the prestack data, leading to a vertical section showing the distribution of most probable facies in an area containing a sand reservoir. We compare the new approach to classification with intercept and gradient. The new method is more robust and less sensitive to the number of attributes (two or three) used for classification. It offers an optimal, flexible, and robust way of extracting the information contained in reflection amplitudes by simple linear AVO equations.