Application of MWD for Shallow Water Demultiple - Hibernia Case Study

Jin, Hongzheng; Yang, Min; Wang, P.; Huang, Yan; Parry, Mervyn; Paisant-Allen, Yvonne A

doi:10.3997/2214-4609.20148450

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…A theoretical solution for progressively incorporating the higher terms is to perform SRME iteratively (Berkhout and Verschuur, 1997). Consequently the iterative SRME approach has been adopted for variants of the water layer modelled approach described above by various workers (for example, Jin et al, 2012) although there is an additional compute cost and the performance of the adaptive subtraction step for each iteration may be data dependent affecting the next one.…”

Section: Methodsmentioning

confidence: 99%

Shallow Reverberation Prediction Methodology with SRME

Barnes¹,

Hegge²,

Borselen³

et al. 2014

Proceedings

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It is well known that surface-related multiple elimination (SRME) breaks down when applied to shallow water datasets. The prediction is distorted at the reconstruction stage by the NMO stretch of the seabed, progressing to the loss of seabed information beyond the critical distance. Furthermore, the adaptive subtraction (multiple elimination) struggles when several orders of the predicted short period reverberation are present, within a given design window for minimization, as the predicted amplitude (and phase) between multiple orders from a single convolution of the data with itself are incorrect. This abstract describes a novel seabed modelled SRME approach with regards to predicting simultaneously and non-iteratively both the amplitude and phase of simple and pegleg source and receiverside sea layer reverberation correctly with minimal distortion for moderately undulating shallow seabeds. Using a shallow water dataset from the Central North Sea, it is demonstrated that the 3D approach can replace more limited 1D τ-p shot-based deterministic multiple prediction techniques to form part of a multi-model multiple prediction strategy that includes iterative SRME where appropriate.

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Section: Methodsmentioning

confidence: 99%

Shallow Reverberation Prediction Methodology with SRME

Barnes¹,

Hegge²,

Borselen³

et al. 2014

Proceedings

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show abstract

“…MWD effectively attenuates WLRMs and thus is a good supplement to SRME for surface multiple attenuation in the shallow water environment (Wang et al, 2011(Wang et al, , 2014. MWD predicts WLRMs (denoted by ) by convolving the Green's function, , of the water layer with recorded data, (Jin et al, 2012;Wang et al, 2011Wang et al, , 2014, , = ∑ ( , )⨂ ( , )…”

Section: Introductionmentioning

confidence: 99%

“…Depending on the surface locations over which the summation is performed, we can either apply 2D MWD (Jin et al, 2012) or 3D MWD (Goss et al, 2013). With one-gun-one-cable input, 2D MWD effectively attenuates most WLRMs.…”

Section: Introductionmentioning

confidence: 99%

Improving shallow water MWD: Offshore Canada case study

Chang

Luo

Shu

et al. 2015

SEG Technical Program Expanded Abstracts 2015

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Data from towed streamer surveys in shallow water environments are usually contaminated by different orders of sea floor reflections called water-layer-related multiples (WLRMs). The model-based water-layer demultiple (MWD) method effectively removes WLRMs. The initial development of 2D MWD was followed by a 3D implementation to better handle out-of-plane WLRMs. This 3D implementation was further improved by using a selective-input strategy to handle the inconsistency of the high-frequency multiple patterns between adjacent sail lines and shots. Time-variant aperture scheme is proposed based on multiple contribution gathers (MCGs) to include sufficient large apertures, avoid aliasing problems, and compensate for apex shifting. Using narrow azimuth towed streamer (NATS) data from the Hibernia field, we demonstrate the benefit of selective-input MWD with timevariant apertures for attenuating WLRMs in shallow water data.

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Closed-loop surface-related multiple estimation with full-wavefield migration-reconstructed near offsets for shallow water

Zhang

Verschuur

2021

GEOPHYSICS

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Reliably separating primary and multiple reflections in a shallow water environment (i.e., 50 m to 200 m water depth) still remains a challenge. The success of previously published closed-loop surface-related multiple estimation (CL-SRME) depends heavily on the data coverage, i.e., the near-offset reconstruction. Therefore, we propose the integrated framework of CL-SRME and full-wavefield migration (FWM). Multiples recorded in the data are capable of helping infill the acquisition imprint of the FWM image. With this image as a strong constraint, we are able to reconstruct the data at near-offsets, which is essential for better primary and multiple estimation during CL-SRME. FWM applied in a non-linear way can avoid the negative influences from the missing data, and at the same time bring in more physics between primaries and multiples. The FWM image of the top part of the subsurface is also used to back-project the information from multiples to primaries with the physical constraint of all this information belongs to the same earth model, provided that a good description of the source wavefield and a reasonable velocity model are available. The proposed integrated framework first reconstructs near-offsets via the closed-loop imaging process of FWM and then feeds the complete reconstructed data to CL-SRME for better primary and multiple estimation. A good performance is demonstrated on both 2D synthetic and field data examples in a challenging shallow water environment.

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Application of MWD for Shallow Water Demultiple - Hibernia Case Study

Cited by 4 publications

References 7 publications

Shallow Reverberation Prediction Methodology with SRME

Shallow Reverberation Prediction Methodology with SRME

Improving shallow water MWD: Offshore Canada case study

Closed-loop surface-related multiple estimation with full-wavefield migration-reconstructed near offsets for shallow water

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