Integrating the Q-factor well-to-seismic tie with a k-means clustering evaluation for enhanced geologic interpretation; a case study of the North Viking Graben data set in the North Sea

This work investigates the influence of precise Q-factor estimation on seismic wavelet estimation, which plays an essential role in rectifying attenuation and distortion in subsurface feature mapping. The Q-factor has a substantial impact on the identification of geologic characteristics and the alignment of seismic and well-log data. We use the peak frequency shift (PFS) methodology, incorporating a redatuming operator to change the Q-factor, in conjunction with sparse-spike and homomorphic deconvolution methods to determine the wavelet. The aforementioned techniques are used on a 2D seismic line located in the North Viking Graben, North Sea. This analysis is combined with the well data and verified through the process of comparing the well data with seismic data. The incorporation of the redatuming operator into the PFS approach effectively minimized the distortions caused by depth variations, whereas sparse-spike deconvolution demonstrated its superiority in intricate geologic structures. The recovery of reflectivity for each segment enabled the precise characterization of lithology. The study indicates that the accurate prediction of the Q-factor improves the interpretation of seismic data, especially in places with complicated geologic features. The integration of many approaches enhanced the precision of subsurface maps, whereas the application of k-means clustering analysis on the lithology and the pore fluid data yielded a more profound understanding of geologic characteristics and hydrocarbon reservoirs.