3‐C imaging of 3D walk‐away data in regard to preprocessing

Clochard, V.; Nicoletis, L.; Svay-Lucas, J.; Mendes, Mateus; Anjos, L.

doi:10.1190/1.1820438

Cited by 5 publications

(2 citation statements)

References 2 publications

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“…Boelle et al (1998) describe the processing sequence used for processing data acquired over the Oseberg field in the North Sea. Zhang et al (1997), Peron (1998), andChen et al (2000) have developed VSP commondepth point (CDP) mapping methods; Chen and McMechan (1992), Mittet et al (1997), Farmer et al (1997, Clochard et al (1998), andBicquart (1998) applied migration schemes for imaging the borehole data. Landrø (1999) investigates the repeatability issues of the data for time-lapse seismic studies.…”

Section: Introductionmentioning

confidence: 99%

Analyzing three‐component 3D vertical seismic profiling data

Gulati

Stewart

Parkin³

2004

GEOPHYSICS

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A three-component 3D vertical seismic profile (VSP) was acquired over the Blackfoot oil field in Alberta, Canada. The VSP survey was recorded simultaneously with a surface seismic program. The objectives of the VSP were to develop recording logistics, data handling, and processing procedures and to determine if the 3D VSP volumes could image the glauconitic sand reservoir of the Blackfoot field.Dynamite shots from the surface seismic survey, which fell within a 2200-m offset from the recording well, were used in the VSP analysis. The shots were recorded by a string of three-component borehole receivers that was moved seven times, resulting in a receiver depth range of 400 to 910 m. The borehole data were processed using basic VSP processing techniques that included hodogram analysis, wavefield separation using median filters, and VSP deconvolution. The final P-P and P-S image volumes were obtained by VSP common-depth point and VSP common-conversion point stacking the upgoing wavefields followed by f -x y deconvolution. The P-P and P-S images from the VSP correlate well with those from the surface seismic survey. Time slices from the VSP also indicate the trend of the sand channel of the Blackfoot field.

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Section: Introductionmentioning

confidence: 99%

Analyzing three‐component 3D vertical seismic profiling data

Gulati

Stewart

Parkin³

2004

GEOPHYSICS

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“…Nevertheless, to achieve a more efficient computation, it is current practice to shoot rays only from a few virtual sources distributed regularly on the model surface and to estimate the traveltime, amplitude and other ray parameters on a coarse regular 3D grid. Using this scheme it is first necessary to interpolate the Green's function values for any real position of the source or receiver and then to resample the traveltimes and the migration/inversion kernel values, in order to obtain a finer spatial grid in the target region only ( Thierry et al ., 1996 ; Clochard et al . 1997 ).…”

Section: Introductionmentioning

confidence: 99%

Green's function interpolations for prestack imaging

Mendes¹

2000

Geophysical Prospecting

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A new interpolation method is presented to estimate the Green's function values, taking into account the migration/inversion accuracy requirements and the trade‐off between resolution and computing costs. The fundamental tool used for this technique is the Dix hyperbolic equation (DHE). The procedure, when applied to evaluate the Green's function for a real source position, uses the DHE to derive the root‐mean‐square velocity, vRMS, from the precomputed traveltimes for the nearest virtual sources, and by linear interpolation generates vRMS for the real source. Then, by applying the DHE again, the required traveltimes and geometrical spreading can be estimated. The inversion of synthetic data demonstrates that the new interpolation yields excellent results which give a better qualitative and quantitative resolution of the imaging sections, compared with those carried out by conventional linear interpolation. Furthermore, the application to synthetic and real data demonstrates the ability of the technique to interpolate Green's functions from widely spaced virtual sources. Thus the proposed interpolation, besides improving the imaging results, also reduces the overall CPU time and the hard disk space required, hence decreasing the computational effort of the imaging algorithms.

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