Pushing the limits of resolution at Holstein: A case history from the deepwater Gulf of Mexico

Calvert, Alex; Ekstrand, Eric; McLain, Bill; Etgen, John; Billette, Frédéric; Sen, Vikram; Regone, Carl; Byrd, Tom; Truxillo, Mark; Young, S.; Cobo, Yannick

doi:10.1190/1.1614156

Cited by 8 publications

(7 citation statements)

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“…Figure 5.20 illustrates the combined effect as measured in an experiment discussed by Calvert et al (2003). In conventional acquisition, one can do little about the notches in the spectrum; therefore, the first notch constitutes the maximum frequency that is useful in general.…”

Section: Ghost Effectmentioning

confidence: 97%

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3D Seismic Survey Design, Second edition

Vermeer¹

2012

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Section: Ghost Effectmentioning

confidence: 97%

“…Note the progressive increase in lateral resolution with decreasing bin size (fromCalvert et al, 2003).…”

mentioning

confidence: 97%

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3D Seismic Survey Design, Second edition

Vermeer¹

2012

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“…In recent years, popular Q systems (Shen, 2004) (Q-land, Q-marine, Q-reservoir, and Q-seabed) (Baeten et al, 2000a(Baeten et al, , 2000b, based on single geophone and integrated with seismic data acquisition and processing, can achieve good data from the subsurface. It is an efficient method which improves the capability of seismic exploration and the vertical and lateral resolution of seismic data (Calvert et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Characteristics analysis on high density spatial sampling seismic data

et al. 2006

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China's continental deposition basins are characterized by complex geological structures and various reservoir lithologies. Therefore, high precision exploration methods are needed. High density spatial sampling is a new technology to increase the accuracy of seismic exploration. We briefly discuss point source and receiver technology, analyze the high density spatial sampling in situ method, introduce the symmetric sampling principles presented by Gijs J. O. Vermeer, and discuss high density spatial sampling technology from the point of view of wave field continuity. We emphasize the analysis of the high density spatial sampling characteristics, including the high density first break advantages for investigation of near surface structure, improving static correction precision, the use of dense receiver spacing at short offsets to increase the effective coverage at shallow depth, and the accuracy of reflection imaging. Coherent noise is not aliased and the noise analysis precision and suppression increases as a result. High density spatial sampling enhances wave field continuity and the accuracy of various mathematical transforms, which benefits wave field separation. Finally, we point out that the difficult part of high density spatial sampling technology is the data processing. More research needs to be done on the methods of analyzing and processing huge amounts of seismic data.

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“…SRME is commonly practised as a two-step process. Because even relatively weak residual multiples can cause serious interpretation difficulties in seismic data sets (Hadidi, Baumstein and Kim 2002;Calvert et al 2003), geophysicists strive to minimize them. Secondly, the predicted multiples are adaptively subtracted from the original traces.…”

Section: Introductionmentioning

confidence: 99%

The Impact Of Field Survey Characteristics On Surface-Related Multiple Attenuation

Dragoset¹,

Moore²,

Kostov

2004

All Days

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Field survey characteristics can have an important impact on the quality of multiples predicted by surface-related multiple elimination (SRME) algorithms. This paper examines the effects of three particular characteristics: inline spatial sampling, source stability, and cable feathering. Inadequate spatial sampling causes aliasing artifacts. These can be reduced by f-k filtering at the expense of limiting the bandwidth in the predicted multiples. Source signature variations create artifacts in predicted multiples due to spatial discontinuities. Variations from a well-behaved air-gun array produced artifacts having an rms amplitude about 26 dB below the rms amplitude of multiples predicted with no variations. Cable feathering has a large impact on the timing errors in multiples predicted by 2-D SRME when it is applied in areas having cross dip. All of these problems can be reduced by a combination of better survey design, use of advanced data acquisition technologies, and additional data processing steps. Introduction In this paper we assume that surface-related multiple elimination (or SRME) is the algorithm of choice for removing multiples from marine seismic data. SRME is commonly practiced as a two-step process. First, surface multiples are predicted by a multidimensional convolutionlike iterative process in which the prediction operators consist of the recorded seismic traces themselves.1 Second, the predicted multiples are adaptively subtracted from the original traces. Under ideal conditions SRME can produce nearly perfect multiple attenuation in a single inversion-like step. Under realistic conditions, however, such an inversion usually produces poor results because of errors that occur in the multiple prediction. Adaptive subtraction and the two-step process attempt to compensate for those errors. Residual multiples occur when the errors in the predicted multiples are too large or too variable to be handled adequately by adaptive subtraction. Since even relatively weak residual multiples can cause serious interpretation difficulties in seismic data sets, geophysicists strive to minimize them. Although the SRME multiple prediction algorithm requires no information about the subsurface geology, it does place strict requirements on the data acquisition process.2 For example, SRME requires full knowledge of the acquisition wavelet and complete, regular spatial sampling of the surface wavefield. Most seismic surveys fail to meet these requirements, resulting in the aforementioned errors in predicted surface multiples. There are three ways of reducing these errors: more appropriate data acquisition practices and survey design, enhancements to the multiple prediction and adaptive subtraction processing algorithms, and application of pre-processing steps to tailor the data for multiple prediction. To select the best method for reducing a particular type of error requires an understanding of exactly how that error is related to various field survey characteristics. Suppose, for example, that a geophysicist is designing a 3-D seismic survey in an area where the direction of the prevailing currents is perpendicular to the predominant dip direction. Is better error reduction achieved by cross-dip shooting, thereby minimizing the effects of cable feathering, or by dip shooting, thereby minimizing subsurface-related 3-D effects in the recorded wavefield? This question can be answered only after a careful analysis of the errors expected in the predicted multiples.

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Pushing the limits of resolution at Holstein: A case history from the deepwater Gulf of Mexico

Cited by 8 publications

References 1 publication

3D Seismic Survey Design, Second edition

3D Seismic Survey Design, Second edition

Characteristics analysis on high density spatial sampling seismic data

The Impact Of Field Survey Characteristics On Surface-Related Multiple Attenuation

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