Multicomponent seismic data may hold a wealth of information for oil exploration and reservoir characterization. Multicomponent seismic contains energy from converted waves that is not seen in conventional seismic; therefore, the development of new techniques to process converted-wave data is important. Much progress has been made in many areas of converted-wave seismic processing, such as stacking, DMO, migration and velocity analysis Alfaraj (1992); Harrison (1990); Huub Den Rooijen (1991); Iverson et al. (1989); Tessmer and Behle (1988). However, more advanced techniques for single-mode PP seismic still have few converted-wave counterparts.
Common-azimuth migration is an efficient and robust technique for obtaining accurate single-mode PP 3-D seismic images. This technique takes advantage of the reduced dimensionality of the computational domain. It assumes that the data have only the zero cross-line offset; that is, all the traces in the data share the same azimuth Biondi and Palacharla (1996). Due to the growing number of 3-D multicomponent seismic data sets in areas where an accurate processing is required to obtain better subsurface images and/or estimate rock properties, wavefield-based continuation methods, such as common-azimuth migration, for converted-wave data are of great importance and are very much needed in the oil industry today.
Rosales and Biondi (2005) introduced the PS-CAM operator. Rosales and Biondi (2002a) first introduced the PS-AMO operator and later Rosales and Biondi (2002b) discussed the geometry regularization problem for converted-wave data. This problem is solved in the least-squares sense. Later on, Rosales and Clapp (2006) present a more accurate transformation from a 5 dimensions prestack data cube into a 4 dimensions data cube through the reduction of the crossline offset for converted-wave data.
This paper focuses on the final image and compares the results on real 3-D Ocean Bottom Seismic data PS image from the Alba oil field. The two process that we present are: 1. Normal Moveout plus stacking along the crossline direction and PS-CAM, we refer to this process as PS-NoMoRe. 2. Data reduction along the crossline direction with the PS-AMO operator and PS-CAM, we refer to this process as PS-AMORe. The final results show that the PS-AMORe method produces images with more coherent reflectors along the reservoir level. Also, geological features (i.e. channels, faults) are now clear and easy to follow and interpret after PS-AMORe.