Journal Article10.3997/1365-2397.2014002
A model-based water-layer demultiple algorithm
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TL;DR: In this paper, a model-based Water-layer Demultiple (MWD) algorithm is proposed to calculate the Green's functions of the Water-Layer Primary Reflections (WLPRs) based on the known seabed and water-layer velocity model and then convolve them with the recorded data to predict the WLRMs.
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Abstract: This paper focuses on the attenuation of Water-Layer-Related Multiples (WLRMs or peg-leg multiples) which reflect at least once between the water bottom and the water surface. WLRMs are often the most dominant multiples in shallow-water seismic data. We propose a Model-based Water-layer Demultiple (MWD) algorithm to calculate the Green's functions of the Water-Layer Primary Reflections (WLPRs: Green's functions convolved with source signature) based on the known seabed and water-layer velocity model and then convolve them with the recorded data to predict the WLRMs. Combined with adaptive subtraction, MWD can effectively attenuate WLRMs. We apply MWD to field data from the Hibernia oilfield area which has a water depth of 70-90 m. The results show that while Surface-Related Multiple Elimination (SRME) by itself has limited success, MWD is effective in attacking WLRMs. Once the WLRMs have been removed by MWD, successive SRME can then be applied to predict and eliminate other types of surface-related multiples (SRMs).�The combination of MWD and SRME is demonstrated as an effective multiple attenuation package for shallow-water data and results in fewer residual multiples and better preserved pri- maries over tau-p gapped deconvolution. This, in turn, contributes to a more realistic velocity model and higher-quality images. from the auto-correlation, to predict WLRMs. DWD pre- dicts WLRMs with correct amplitudes. However, when the water-bottom is complex (and thus multi-arrivals of WLPRs present) the water-layer model derived from the time-domain
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Citations
Multiple attenuation for shallow-water surveys: Notes on old challenges and new opportunities
Clement Kostov,Frederico Xavier de Melo,Abhishek Raj,Alexander Zarkhidze,Alex Cooke,Glenn Miers,John Bacon +6 more
TL;DR: A proposed workflow improves on 3D SRME results for shallow-water surveys while aiming to remove all surface-related multiples rather than just a subset from those multiples.
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Seismic multiple suppression based on a deep neural network method for marine data
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TL;DR: In this article , a method of primary reconstruction using a deep neural network based on data augmentation training is proposed to attenuate multiples with background noise, which can affect the identification of oil and gas reservoirs.
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Seismic expression of delta to deep-lake transition and its control on lithology, total organic content, brittleness, and shale-gas sweet spots in Triassic Yanchang Formation, southern Ordos Basin, China
TL;DR: In this paper, a seismic-sedimentological study using a newly acquired 3D survey was conducted to advance the interpretation and prediction of the shale-gas reservoirs in the Ordos Basin, China.
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Patent
Method and apparatus for modeling and separation of primaries and multiples using multi-order green's function
Gordon Poole,Richard Wombell,James Cooper +2 more
- 14 Apr 2015
TL;DR: In this paper, the authors used a multi-order Green's function to detect the primary waves contained in the received data, which is indicative of the primary signals contained in an underground formation.
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Selective-input Adaptation of Model-based Water-layer Demultiple
TL;DR: In this paper, a selective-input adaptation of regular 3D MWD workflow that uses both regularized shot gathers and regularized common-offset cubes as input and prioritizes the data selected for model prediction is proposed.
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References
Adaptive surface-related multiple elimination
TL;DR: In this paper, a method for the elimination of all surface-related multiples by means of a process that removes the influence of the surface reflectivity from the data is proposed.
Attenuation of complex water-bottom multiples by wave-equation-based prediction and subtraction
TL;DR: In this article, the authors derived the expressions needed for predicting multiple reflections that are generated by the water bottom in marine seismic data, in a form that can be used to estimate the reflectivity of a water bottom that has a complex shape and has a reflectivity that varies with lateral position, frequency and reflection angle.
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