In this work, we have performed model building and 3D PreSDM on P-wave data from a 3D OBC survey using offset vector tile (OVT) processing, wherein the velocity is estimated by a surface fitting non-sectored approach, characterising velocity in terms of a fast and slow direction, along with the azimuth of the fast axis. Using OVT VTI tomographic inversion permits us to thereby better resolve localized heterogeneity that is usually left unresolved when using a conventional non-OVT tomographic approach. In addition to the OVT VTI tomography, we followed the 3D PreSDM model building and migration with an HTI azimuthal velocity analysis in an attempt to characterize any dominant fracture patters: in this case, no dominant patterns were f ound. We compare the results with existing data showing how rigorous handling of azimuths better constrains the lateral velocity variations and results in a better focused image in the Jurassic, especially of steep fault planes.
The compensation of absorption loss inside the imaging process using attenuation models estimated by Qtomography is now widely accepted and used in the industry. This technology becomes even more important in the case of a complex dataset. For the Martin Linge field, characterized by a strong presence of faults and gas clouds, the multi-azimuth broadband acquisition involving a variable-depth streamer has helped to improve the quality of the data. High-end processing and imaging so far provided an image with enhanced resolution compared to legacy data mainly with regard to faulting in the deeper area. Nevertheless, imaging remained poor in the deeper part of the section because of a seismic obscured area (SOA) caused by gas clouds. In this paper we now illustrate how we managed to enhance the resolution under this SOA zone using volumetric Q-tomography and Q-prestack depth migration (Q-PSDM). In other words, we show that multi-azimuth broadband acquisition combined with Q-tomography/Q-PSDM techniques can provide an improved final image in the case of a complex data with a SOA.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.