The world's demand for energy is accelerating, while its reserves of energy are diminishing. Producers are compelled to explore and produce oil and gas in more challenging environments and to maximize recovery in existing reservoirs. New seismic exploration technology has always been a key to success. One such new technology is Ocean Bottom Station (OBS) nodes.
Since the cost of OBS deployment is high, a practical and relatively economical geometry for OBS is a sparse grid of nodes and a dense grid of shots. However, the sparse node geometry provides poor illumination, especially of reflectors whose depth under the seabed is less than the node interval. Fortunately, there is a good solution for this problem: the mirror imaging method. The sea surface acts as an acoustic convex mirror reflecting the image of subsurface structure. This mirror image offers wider illumination than a conventional image derived from primary reflections. We present the mirror imaging method and compare conventional and mirror imaging results from OBS data recorded in the North Sea, the Norwegian Sea and offshore West Africa. We then discuss the reasons why mirror imaging is superior to the conventional image of the primaries.
Ocean Bottom Station (OBS) nodes (Berg et al., 1994; Ronen et al., 2003; Amal et al., 2005; Docherty et al., 2005; Granger et al., 2005) are an emerging seismic exploration technology. Surface-towed streamers provide excellent seismic data for exploration, development and production monitoring. However, streamers have limitations and this motivates a quest for alternative technologies. When using streamers, obstacles such as production platforms require undershooting and the data lack near offsets and have anomalous azimuth distributions. OBS nodes are much less sensitive to obstacles and allow more complete coverage. When deployed by a Remotely-Operated Vehicle (ROV), they can be placed very accurately, right up to, or even under, production installations.
Obstacles are not the only motivation for OBS technology. Even in the absence of obstacles, OBS nodes facilitate wide-azimuth geometries, important for imaging structures under complex overburdens such as salt environments. In particular, by moving the receivers to the seabed and recording a dense shooting grid on the sea surface, we can create a dataset that is suitable for wave-equation migration, is well-populated in azimuth and offset, and provides optimal subsurface illumination; conventional towed-streamer acquisition cannot provide this type of dataset because of the constraints imposed by the fixed source-receiver geometry. Indeed, while the Wide-Azimuth Towed-Streamer (WATS) method is becoming popular, and can also provide data suitable for wave-equation migration (Threadgold et al., 2006), it requires separate source vessels as well as repeated shot lines with different streamer locations. Such effort comes at a cost, and, for small areas (up to about 400 sq.km), OBS technology can be less expensive than WATS.
Similarly, monitoring with time-lapse seismic surveys (4D) requires acquisition repeatability; OBS nodes deployed by ROVs provide better repeatability than streamers, which are subject to feathering due to currents. Indeed, although the limitations of streamers are reduced with streamer-steering, streamer-steering can only have a small effect of just a few degrees, while feathering is sometimes ten degrees or more. Eiken et al. (2003) report a natural streamer feathering within +/- 6o for 95% of Norwegian Sea operations, and a streamer steering capability of +/- 3o that is not sufficient to achieve a zero-feather survey in a cost effective manner. Streamer steering also acts as an additional source of noise in seismic recordings.
Finally, nodes deployed on the seabed record not only P-waves but also S-waves; these are a useful complement to P-waves but do not travel in water, and therefore cannot be recorded by surface-towed streamers. OBS nodes can provide four-component (4C) seismic data from a hydrophone and a three-component geophone, thereby enabling elastic-wave analysis, as well as wavefield separation and multiple removal. Applications of elastic-wave analysis include imaging beneath gas clouds, imaging low P-impedance reservoirs and fracture characterization.