Spectral Decomposition of Seismic Reflection Data to Detect Gas Related Frequency Anomalies

A study was conducted into the use of spectral decomposition algorithms for the purpose of direct hydrocarbon detection through frequency anomalies in a geologically and seismically challenging area. A shallow water North Sea seismic data set in an area with well-known geology, with good well control for both water and hydrocarbon bearing formations was used as test set in this study. The correlations found between attribute responses and well results can be used to de-risk leads and prospects. The study included synthetic modeling with two objectives: assessing the relative performance of different spectral decomposition algorithms, and obtaining indications on algorithm sensitivity, especially for changes in rock properties such as porosity and saturation. Subsequently the most promising spectral decomposition algorithms were applied to the real seismic dataset. The results of this step were cross-validated with the synthetic study and interpreted using the geological information in the area as constraints.