Abstract
Fast Beam Migration (FBM) is a super-efficient algorithm that is two orders of magnitude faster than the standard Kirchhoff depth migration, and at the same time images multi-pathing energy, a property that is typically associated with wave-equation migration algorithms. The faster imaging step allows for more iterations of velocity model building (50–100 iterations, instead of the current 7–10), which enable the processing team to enhance the seismic resolution and imaging of complex geologic structures. Improved velocity models in combination with FBM or wave-equation imaging can provide much greater resolution and accuracy than what can be accomplished today with standard imaging technology. This advanced imaging methodology will improve success rate and cost effectiveness for new deep-field discoveries, greatly reduce the turnaround time for large surveys, and also have applications in increasing recovery efficiency for the development of existing fields, or 4-D seismic monitoring of CO2 injection and sequestration projects. This technology does not exist widely in the industry, is a fundamental advance, and is a necessary building block in any seismic processing system that uses wave-equation methods for imaging ultra-deep land and water, complex oil-and-gas reservoirs.
The idea of beam-based seismic imaging algorithms is not new. Hill (1990, 2001) developed a rigorous and powerful method of depth imaging following the classical Gaussian beam construction. This work has been extended in different ways by a number of researchers (da Costa et al., 1989; Nowack et al., 2003; Gray, 2005). The idea of fast beam migration was also explored by Gao et al. (2006) and found perhaps the most successful commercial implementation at Applied Geophysical Services (Masters and Sherwood, 2005; Sherwood et al., 2009). While building on top of the previous knowledge, we developed a principally new implementation of fast beam-based seismic imaging. Our approach is based on the following novel ideas:
An innovative method of beam forming by using automatic plane-wave destruction (Fomel, 2002). This method is the working engine in the Data Decomposition via Beam Forming part of the algorithm.
An innovative method of beam extrapolation and imaging. The method derives from ideas employed previously in wavepath and parsimonious migration (Sun and Schuster, 2003; Hua and McMechan, 2005) and oriented imaging (Fomel, 2003, 2007b).
In practice, the Fast Beam algorithm achieves its speed by using the dip information pre-computed from pre-stack data, in two steps: (1) a factor of 10–100 in speedup is achieved via beam forming, or beam decomposition of the input data, where the number of input data traces is reduced by a factor of 10–100; (2) a factor of 10–100 in speedup is obtained by spreading each input trace, or beam over a beam patch instead of a full aperture-volume, by using the approximate dip information. Thus a single sample is spread over a beam patch instead of a full ellipsoid surface. The combined speed up gives a factor of 100–1,000 in decreased run-time. The performance of the FBM algorithm allows us to migrate a 3000 square kilometers dataset on 1000 CPUs in 20–30 minutes, enabling a truly interactive migration for velocity model building definition and refinement.
