L Lo oc ca al l f fr re eq qu ue en nc cy y a as s a a d di ir re ec ct t h hy yd dr ro oc ca ar rb bo on n i in nd di ic ca at to or r L Lo oc ca al l f fr re eq qu ue en nc cy y a as s a a d di ir re ec ct t h hy yd dr ro oc ca ar rb bo on n i in nd di ic ca at to or r S Sh he en ng gh ho on ng g T Ta ai i, , C Ch ha ar rl le es s P Pu ur ry ye ea ar r, , J Jo oh hn n P P. . C Ca as st ta ag gn na a, , U Un ni iv ve er rs si it ty y o of f H Ho ou us st to on n. . SummaryAs a seismic wave propagates, it loses energy due to spherical divergence, scattering, intrinsic absorption and reflection at interfaces where rock properties change. The amplitude and frequency responses of the reflected seismic wave are influenced by a variety of factors including: geologic structure, layer thickness, lithology, and pore fluid properties. When the seismic wave travels back to the surface, it also bring back the information related to stratigraphic features, rock property changes and hydrocarbon accumulations. Each reservoir has its own characteristic seismic frequency response because of its unique rock and fluid properties discriminating it from the surrounding environment. We utilize a spectral decomposition method to extract the characteristic frequency components from seismic data and identify low frequency anomalies. To understand the underlying physical factors of the low frequency anomaly, we build a set of wave-equation based synthetic forward modeling. The result of our analysis shows that seismic waves travel more slowly through gas zone than the background material is a main reason for seismic time series delay and low frequency anomaly in the thin layer reservoir. Our explanation has been applied in the analysis of frequency anomalies corresponding to gas-bearing sands in the Gulf of Mexico fields.

Various studies have demonstrated the usefulness of spectral decomposition and its associated frequency attributes in seismic interpretation and hydrocarbon exploration. However, many different techniques for spectral decomposition exist in the petroleum industry, creating a need for comparative studies of these techniques to evaluate their utility. In this work, we compare the results of the application of the CWT and MPD algorithms and associated frequency attributes to a complex turbidite model. Our results indicate that better resolution of stratigraphic features is achieved by the MPD algorithm. These improvements include sharper definition of lateral stratigraphic changes and detection of subtle channel features associated with off-peak frequencies. We also show the effective extraction of stratigraphic features associated with off-peak frequencies achieved by principal component analysis. We believe a quantitative assessment of the relationship between the rock properties volume and frequency attributes will provide useful insight during future work.

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