Dip-angle Image Filtering for 4D Processing of Towed-streamer and OBN Datasets

Ocean-bottom data are often acquired in the development and production stages of an oilfield’s lifecycle to guide well placement and water injection strategy. Although well suited to time-lapse monitoring, this type of ocean-bottom survey will miss the first time-step in which a field is explored, appraised, and undergoes initial fluid production. To capture this time step it is necessary to 4D co-process ocean-bottom data with the exploration dataset, usually surface towed streamer. Our example from the North Sea benefits from flexible trace pairing to produce high-fold subsets of the input data that generate more similar images than would otherwise be available. However, residual multiple generates significant 4D noise, as does un-cancelled migration operator from the very different, irregular, survey geometries. Migration to: (1) common-offset, (2) scattering-angle, and (3) dip-angle output domains provide opportunity to explore similarity-filtering strategies with the data. The scattering- and dip-angle gathers respond well to similarity filtering, but results show greater spatial resolution, signal continuity, and coherence when dip-angle gathers are used. Dip-angle is an intuitive domain in which to locate and attenuate un-cancelled migration operator in 4D, which is advantageous as migration noise is a major source of 4D noise in these data.