An Improved Approach for Hydrocarbon Detection Using Bayesian Inversion of Frequency- and Angle-Dependent Seismic Signatures of Highly Attenuative Reservoirs

Abstract

Studies of frequency dependence of seismic data anomalies on partially gas-saturated reservoir have been performed for many years. Essentially, the frequency-dependent seismic signature represents a potential and largely untapped source of information for the detections of subsurface target properties. Through analyzing the anomalous feathers of amplitude variations with the angle of incidence and frequency (AVAF), both theoretically and algorithmically, it is possible to discriminate hydrocarbon from variations in other reservoir properties. For a layered structure model, however, it can be challenging to employ the conventional Zoeppritz equation-based method that may not accurately describe complex reflections considering the effects of both the layered structure of a reservoir and the attenuative and dispersive property of rocks. We introduce a novel hydrocarbon detection approach based on Bayesian inversion of frequency- and angle-dependent reflection signatures from a tight gas sandstone reservoir having strong attenuation and velocity dispersion. The proposed inversion scheme employs the propagator matrix method as a description of seismic responses for the stratified model and spectral decomposition technique to obtain multifrequency amplitude information. The synthetic test and real application show the proposed inversion approach has the potential to be useful in detections of hydrocarbon accumulation.