3D SRME application in the Gulf of Mexico

Lin, Dechun; Young, Jerry; Huang, Yan; Hartmann, Monica

doi:10.1190/1.1851098

Cited by 23 publications

(6 citation statements)

References 4 publications

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“…For SRME, predicted multiples often include source signatures and directivity patterns that differ from those present in the data ͑see, e.g., Verschuur et al, 1992;Ikelle et al, 1997͒. Moreover, 2D SRME produces errors in the predicted multiples because of 3D complexity of the earth ͑Dragoset and Jeričević, 1998; Ross et al, 1999;Verschuur, 2006͒, whereas recently developed full 3D-SRME algorithms can suffer from imperfections related to incomplete acquisitions ͑see, e.g., Lin et al, 2004;Moore and Dragoset, 2004;van Borselen et al, 2004;van Dedem and Verschuur, 2005͒, including erroneous reconstructions of missing near offsets ͑Dragoset and Jeričević, 1998͒. For field data, these factors preclude iterative SRME, resulting in amplitude errors that vary for different multiple orders ͑see, e.g., Verschuur and Berkhout, 1997;Paffenholz et al, 2002͒. In practice, the second separation stage appears to be particularly challenging because adaptive ᐉ 2 -matched-filtering techniques are known to lead to residual multiple energy, high-frequency clutter, and deterioration of the primaries ͑Chen et al, 2004;Abma et al, 2005;Herrmann et al, 2007a͒.…”

Section: Introductionmentioning

confidence: 99%

Adaptive curvelet-domain primary-multiple separation

2008

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In many exploration areas, successful separation of primaries and multiples greatly determines the quality of seismic imaging. Despite major advances made by surface-related multiple elimination ͑SRME͒, amplitude errors in the predicted multiples remain a problem. When these errors vary for each type of multiple in different ways ͑as a function of offset, time, and dip͒, they pose a serious challenge for conventional least-squares matching and for the recently introduced separation by curvelet-domain thresholding. We propose a data-adaptive method that corrects amplitude errors, which vary smoothly as a function of location, scale ͑fre-quency band͒, and angle. With this method, the amplitudes can be corrected by an elementwise curvelet-domain scaling of the predicted multiples. We show that this scaling leads to successful estimation of primaries, despite amplitude, sign, timing, and phase errors in the predicted multiples. Our results on synthetic and real data show distinct improvements over conventional least-squares matching in terms of better suppression of multiple energy and high-frequency clutter and better recovery of estimated primaries.

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Section: Introductionmentioning

confidence: 99%

Adaptive curvelet-domain primary-multiple separation

2008

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“…Typical marine or offshore acquisitions are often inadequately sampled in the crossline shot direction, and this introduces aliasing in the demultipled data [19] if not addressed. Various strategies have been suggested to address this, such as shot interpolation [20,21,22], interpolation of the missing contributions from the multiple model [23] and dense acquisition, all of which would raise hardware compute capacity and run time and therefore operational costs.…”

Section: Introductionmentioning

confidence: 99%

Free Surface Multiple Removal Using 3D Surface Related Multiple Elimination Technique on 3D Seismic Data from Offshore Niger Delta

Ogagarue¹,

C.N²

2019

JGG

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In addition to de-spiking, gun delay correction, correction for spherical spreading and earth's absorption, gun and cable correction, zero-phasing, noise attenuation and correction for time shifts between sail lines due to changes in velocity of the water column, multiple elimination is the other important true relative amplitude processing routine desired to produce seismic gathers consistent with DHI and AVO analysis to de-risk the presence of hydrocarbon interpreted from seismic data. Multiple problems associated with seismic data acquired in the Niger Delta offshore are those due mainly to the free air-water interface and the seabed, and their presence constitute noise in the seismic dataset. In this study, we employed an approach in which we convolved all possible source and receiver peglegs for every multiple event that strikes the free surface irrespective of its path in the subsurface to model the multiple wave field in a 3D sense, in a partially processed pre-migration seismic data acquired in the Niger Delta deep offshore. The method of adaptive subtraction was then used to eliminate the modeled multiples from the dataset. Unlike other demultiple techniques such as tau-p deconvolution and radon, our multiple modeling and subtraction techniques do not require prior knowledge of the subsurface geology in terms of the velocity and reflectivity of the multiple wave field. The aim of the study was to improve the overall quality of the seismic data and signal-to-noise ratio, in addition to the DHI and AVO compliant gathers output from the process. The approach was successful and effective in removal of the free, air-water surface multiples from the dataset.

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“…However, the lack of data in the crossline direction makes 3D ISS prediction difficult for conventional streamer data. We overcome this difficulty by constructing high-density and wide-azimuth data from the existing streamer geometry, the same approach that was used in 3D SRME (Lin et al, 2004) and 3D internal multiple attenuation (Hung et al, 2013). In this paper, we discuss the implementation of 3D ISS internal multiple attenuation and apply the method on synthetic and real seismic datasets.…”

Section: Introductionmentioning

confidence: 99%