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Results
Wave-Equation Angle-Based Illumination Weighting Study: Thunder Horse, Gulf Of Mexico
Gherasim, Mariana (EPT BP) | Albertin, Uwe (EPT BP) | Nolte, Bertram (EPT BP) | Etgen, John (EPT BP) | Vu, Phuong (EPT BP) | Jilek, Petr (EPT BP) | Trout, Matt (GOM BP) | Hartman, Ken (GOM BP)
We begin by comparing by slant -stacking, gives illumination information in the our wave-equation illumination weighting workflows, angle direction (Askim et al., 2010). We then use these explain the differences between the one-way and two-way illumination gathers to generate corresponding illumination methods and describe the well tie procedure. We then weight gathers, which improve subsalt structural images demonstrate the impact of our illumination weighting when applied to migrated angle gathers.
- North America > United States > Gulf of Mexico > Central GOM > East Gulf Coast Tertiary Basin > Mississippi Canyon > Block 822 > Thunder Horse Field (0.99)
- North America > United States > Gulf of Mexico > Central GOM > East Gulf Coast Tertiary Basin > Mississippi Canyon > Block 778 > Thunder Horse North Field (0.99)
- North America > United States > Gulf of Mexico > Central GOM > East Gulf Coast Tertiary Basin > Mississippi Canyon > Block 778 > Thunder Horse Field (0.99)
- (3 more...)
Efficient Reflection Tomography With Ray Take-Off Directions Collected From Beam Migration
Zhang, Qie (BP) | Jilek, Petr (BP) | Etgen, John (BP) | Albertin, Uwe (BP) | Clarke, Richard (BP) | Nolte, Bertram (BP)
Summary Reflection tomography is often run with beam migration for iterative velocity refinement. Both are ray-based algorithms: beam migration shoots rays down from the surface; while tomography shoots rays up from RMO picking points to the surface. The former ray tracing is straightforward with initial directions defined by a fan of preset angles; however, the latter is difficult, similar to a two-point ray tracing problem. Historically ray tracing in migration and tomography are decoupled. But in this study, we will establish a relationship between them, so that ray paths computed in beam migration provide ray take-off directions for reflection tomography. This coupling concept dramatically boosts the efficiency and accuracy of standard ray-based reflection tomography.
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (1.00)
- Geophysics > Seismic Surveying > Seismic Processing > Seismic Migration (0.94)
Beam Steering In Ray-based Beam And Wave-equation Migration
Jilek, Petr (Advanced Imaging, Exploration and Production Technology Group, BP, Houston) | Brandesberg-Dahl, Sverre (Advanced Imaging, Exploration and Production Technology Group, BP, Houston) | Zhou, Min (Advanced Imaging, Exploration and Production Technology Group, BP, Houston) | Albertin, Uwe (Advanced Imaging, Exploration and Production Technology Group, BP, Houston)
SUMMARY One of the most difficult issues facing subsalt imaging is the removal of coherent noise in areas of poor illumination. Recently, ray-based beam migration has been shown to be an effective tool for suppressing such coherent noise. Ray-based beam migration accomplishes this through the use of localized slant-stacks on input data combined with appropriate picking and muting of information in the slant-stack prior to imaging. In contrast, standard wave-equation migration workflows for subsalt imaging typically are unable to suppress such coherent noise. Here we present an alternate workflow for wave equation migration that uses ray-based beam migration as a preprocessing step prior to wave-equation migration. Once appropriate picking has been done on the slant-stacks for ray-based beam migration, we perform inverse slant-stacks of the beam data and use it as input to wave-equation migration. This gives wave-equation migration the same noise-suppression benefits observed in ray-based beam migration. INTRODUCTION Subsalt imaging remains one of the primary challenges facing oil exploration today. In many areas, poor subsalt illumination, strong attenuation, mode conversion, as well as strong surface and internal multiples all contribute to poor subsalt imaging results. The difficulties related to these issues are often seen as coherent noise with conflicting dip in subsalt areas, making a successful interpretation difficult. Recently, advances in ray-based beam imaging have illustrated promise in addressing some of these issues. The initial process in a beam migration is the formation of localized slant-stacked data in either the common-midpoint, common-shot, or common-receiver domains. If all of the data in the slant-stack is migrated, the image will be similar to a standard Kirchhoff or wave-equation (WE) migration, although because beams in a ray-based beam migration move apart according to the underlying raypaths in poor illumination zones, beam migration has a natural mechanism for suppressing common ’smiling’ artifacts associated with Kirchhoff migration. More recently, however, additional noise suppression in beam migration has been made possible by appropriate picking and muting of input slant-stacks based on various criteria to remove coherent noise (Heinze, 2001, Sun and Schuster, 2001). In this regard, beam migration has extraordinary flexibility compared with other migrations because the ray paths used to migrate the slant-stacks carry directional information about how energy is moving at the source, receiver and subsurface imaging locations. Hence muting in the slant-stack prior to imaging can be done based on a number of criteria. Simple muting based on an amplitude or energy threshold can be effective for removing general background noise in the image, as well as smiling artifacts that are associated with beams that actually carry very little energy, but which refract strongly at salt interfaces. Directional information for energy arriving at the surface can be used as a muting criterion to select out arrival energy arriving in a particular direction. Where beam migration has a particular advantage over other migrations, however, is its ability to provide muting criteria based on directional information at the imaging point; no other type of algorithm is able to supply this type of information as efficiently.