Incorporating streamer data for OBN free-surface multiple prediction – A case study in deep-water Gulf of Mexico

Abstract

Ocean Bottom Node (OBN) acquisition has been widely used as a powerful tool for reservoir imaging and monitoring. However, similar to streamer acquisition data, the OBN wavefield contains high-order multiples that can interfere with reservoir primary events. To obtain a clear reservoir image, especially for subsalt targets, effective demultiple for the down-going wavefield is critical. However, due to the considerable cost of semi-permanent deployments, OBN surveys have sparser receiver sampling than that of streamer surveys. This sparse sampling poses challenges in multiple attenuation. Also, the significant datum difference between the source and receiver make the conventional cross-convolution based surface-related multiple elimination (SRME) method inapplicable. By incorporating streamer data, we extend the conventional SRME flow to OBN free-surface (FS) mulitple prediction. We apply this modified flow on an OBN survey from deepwater regions of the Gulf of Mexico (GOM). The result confirms that by combing both streamer and OBN data, FS multiples in OBN down-going wavefield can be effectively predicted and removed, even in areas with complex salt strucutures.