Full-azimuth internal-multiple modeling and attenuation in a deep-water OBN setting
Моделирование и подавление внутренних множественных волн во всех азимутах в условиях глубоководной OBN-съёмки
2026-02-10
SCID: 54.1/pxgtdqrh
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convolutional processdeep-water Santos Basindeterministic generator of internal multiple travel pathfull-azimuthinternal multiple attenuation (IMA)internal-multiple modelingocean-bottom nodes (OBN)presalt imagingsignal-to-noise ratio (SNR)time-lapse seismic
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Abstract (AI)
A field-scale experiment was performed using a combination of 3D internal multiple attenuation (IMA) methods and the deterministic derivation of the generator of the internal multiple travel path in the convolutional process. This innovative approach in the deep-water Santos Basin proved to be beneficial not only for the interpretation of complex structures but also for reservoir monitoring and characterization in time-lapse seismic products, enabled by the improved signal-to-noise ratio (SNR) of the image in presalt areas.
Key Findings
1
A field-scale experiment combined 3D internal multiple attenuation (IMA) methods with deterministic derivation of the internal multiple travel-path generator in the convolutional process.
2
Full-azimuth internal-multiple modeling and attenuation proved beneficial for imaging and analysis in the tested deep-water environment.
3
The combined approach was applied in a deep-water Santos Basin OBN setting and improved interpretation of complex subsurface structures.
4
The method enhanced reservoir monitoring and characterization in time-lapse seismic products by improving image signal-to-noise ratio (SNR) in presalt areas.
Research Object
Full-azimuth internal multiples in deep-water Ocean Bottom Node (OBN) seismic data from the Santos Basin presalt area
Research Subject
Modeling and attenuation (removal) of internal multiples and their impact on image SNR for interpretation, reservoir monitoring and time-lapse seismic characterization using 3D IMA methods and deterministic generator derivation
Publication Details
Publication Date
2026-02-10
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