Summary Subsalt imaging at the Kepler field, located in deep water Gulf of Mexico, has proven to be challenging, largely because of suboptimal illumination and complicated ray bending caused by complex overburden salt. It is well understood that building an accurate velocity model is one of the prerequisites to obtain a good subsalt image. Multiazimuth seismic data sets not only help with improving subsalt illumination, they also benefit the velocity model building process by providing more data to constrain the updates. In this paper, we demonstrate the impact of multi-azimuth data and the Kepler salt geometry on subsalt illumination through a 3D ray-tracing and finite difference modeling illumination study. The integration of a top of salt (TOS) anomaly layer into the Kepler salt velocity model significantly improved the subsalt images and reduced base of salt depth uncertainties.
Elebiju, Bunmi (BP America) | Ariston, Pierre-Olivier (BP America) | van Gestel, Jean-Paul (BP America) | Murphy, Rachel (BP America) | Chakraborty, Samarjit (BP America) | Jansen, Kjetil (BP America) | Rodenberger, Douglas (Shell America) | White, Roy C. (Shell America) | Chen, Yongping (CGG) | Hren, David (CGG) | Hu, Lingli (CGG) | Huang, Yan (CGG)
Using the Kepler and Ariel Fields as a case study, this paper discusses the processing challenges and solutions applied to a 4D co-processing of Wide Azimuth Towed Streamer (WATS) on Narrow Azimuth Towed Streamer (NATS) data. Unlike a dedicated 4D acquisition, WATS on NATS 4D has relatively low repeatability in terms of acquisition geometry and bandwidth differences. All these factors can negatively impact the extraction of a meaningful 4D signal. In this paper, we demonstrate how processing techniques can help to increase repeatability and enhance 4D signal. We focus on the following 4D processing procedures: 4D co-binning, data matching, and post-migration co-denoise. Due largely to these techniques, the final co-processed volumes show an optimized 4D seismic signal with a median Normalized Root Mean Square (NRMS, which measures the repeatability between base and monitor. Details refer to Kragh and Christie, 2002) of 0.10 along the water bottom and 0.28 above the reservoir.