This study shows the results from finite difference modeling for optimizing the acquisition design of the Atlantis 2014-2015 Ocean Bottom Nodes (OBN) survey. During the planning of this Atlantis time lapse acquisition, it was realized that additional nodes would be available, which provided an opportunity to use those nodes to improve the 3D static seismic image. A modeling study was initiated with the objective to identify optimum placement of the additional nodes. The model was generated using realistic detailed stratigraphy modeling and several cases were studied by adding node patches in different directions. Observations show that the addition of the nodes to the South, has the largest impact on the imaging of Atlantis Field. To measure the imaging impact of each added node location a more detailed tool was generated; the node areal contribution map, which shows how much each individual node contributes to the image of the reservoir. Improved imaging was also shown by modeling of reduced node spacing and increased node density. Altogether, the insights from this modeling work enabled optimized acquisition design for the survey that was acquired on the Atlantis Field in 2014-2015.
The Atlantis Field sits about 300 km south of the Louisiana coast in the Gulf of Mexico in around 7000 feet of water (Figure 1). It began production in October 2007 from Middle Miocene turbidite reservoirs that lie about 17,000 feet below sea level. As the field is sitting under the Sigsbee escarpment and the edge of a very complicated salt body with multiple salt fingers, seismic imaging has been challenging (Roberts et al., 2011). Only the southern end that sits outside of the salt can be imaged with confidence.
The primary objective of the 2005-2006 OBN survey was to obtain a consistent, high-quality image of the subsalt portion of the reservoir. This survey was the world’s first large-scale deepwater survey to employ autonomous nodes (Beaudoin and Ross, 2007). OBN technology also allows for operationally highly repeatable time-lapse seismic and overcomes the challenges presented by surface and subsea installations. Therefore, in 2009 a monitor survey was acquired (Reasnor et al., 2010). This time lapse data had excellent geometric repeatability and low 4D noise levels. The survey showed the depletion signature of the field in the time shift and the amplitude response (van Gestel et al., 2013).
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.
Moving from VTI (Vertical Transverse Isotropy) to TTI (Tilted Transverse Isotropy) was arguably the most important factor contributing to the success of the 2009 Mad Dog TTI reprocessing (Bowling et al., 2010). Incorporating data from a previously recorded NATS (narrow-azimuth towed-streamer) survey, oriented 66° from the Puma/Mad Dog WATS (Wide-Azimuth Towed-Streamer) sail direction, provided important additional azimuthal coverage, which allowed for a more robust estimation of the TTI model’s five components (Huang et al., 2008). A secondary benefit of the additional NATS data was the enhanced illumination it provided for both the salt delineation and the subsalt image. However, even with the imaging improvements in the 2009 TTI project, the subsalt image of some poorly illuminated areas remained unclear. Encouraged by the step-change improvement driven by TTI imaging and additional azimuthal coverage, BP initiated a Multi-WATS TTI reprocessing in 2011. In addition to the data sets used in the 2009 reprocessing, additional data sets - including the newly acquired WesternGeco Phase 14 and infill WATS, Vertical Seismic Profiles (VSPs) and new well data - were utilized to improve velocity model building and provide the best possible illumination for the subsalt reservoir. The project was very successful - a better TTI model was achieved and, together with new imaging technologies, the image of the reservoir structure was improved and better positioned. The almost full-azimuth (FAZ) surface data and advanced model building flow also exposed the limitations of TTI modeling. Tests conducted as part of the project suggest that emerging TOR (tilted orthorhombic) modeling may provide an even more accurate approximation of the Mad Dog velocity field.