SEISMIC PROCESSING

We have developed key technologies in incoherent and coherent noise removal, deghosting and designature, data regularisation, imaging, residual moveout and post-migration processing to meet this goal.

We process all types of data: 2D, 3D, 4D, land, marine and OBC. Our largest project to date was 22,000 sq km - a single tomography run over the whole area.

We have state of the art, highly parallel processing codes that take advantage of our supercomputing facilities which utilise the latest in co-processor technology.

DUG’s supercomputers in Perth (Bruce), Houston (Bubba), and Kuala Lumpur (Bhodi) ensure we can deliver large projects with turnaround times faster than you’ve ever imagined.

DUG HAS A COMPREHENSIVE TIME PROCESSING TOOLKIT WHICH INCLUDES:

› DUG Broad - wave-equation-based deghosting and redatuming

› Source signature deconvolution using near-field hydrophone recordings

› SRME and IME - 3D true-azimuth, surface-related and interbed multiple elimination respectively

› DUG SWAMI - 3D shallow water attenuation of multiples by inversion

› Surface consistent processing (statics, amplitudes, deconvolution)

› DUG Reg - 2D/3D/4D/5D interpolation and regularisation (offset-azimuth and COV)

› Various noise removal techniques including multi-dimensional Cadzow filtering

› Wide-azimuth tools including COV processing and anisotropic, azimuthal moveout corrections

› Automatic residual moveout picking and gather flattening

› Comprehensive post-migration workflows

OUR FOCUS IS TO DELIVER BROADBAND DATA OF THE HIGHEST QUALITY - READY FOR INTERPRETATION AND QUANTITATIVE AMPLITUDE STUDIES - TAILORED TO SUIT THE NEEDS OF OUR CLIENTS

DUG recognises many opportunities to use high-quality relevant source signatures, such as signature deconvolution, zero-phasing and removing the source bubble.

Use of a single source signature for these tasks is not optimal, because source signatures vary from shot to shot (due to pressure, timing and drop-outs/misfires) and bubble periods are sensitive to temperature and shooting speed. It is therefore preferable to use the signature information of each shot recorded as the survey is acquired. DUG use the near-field hydrophone data to calculate a notional signature for each shot, allowing shot variations to be accounted for. Far field source signatures can then be calculated as a function of azimuth and emission angle. The resulting far-field source signatures will have an accurate low frequency phase that is consistent with the acquired data (see figure below).

DUG’s method for deriving the notional signature of each source is to apply a conjugate gradient least squares approach to the work of Ziolkowski et al. (1982). This is based upon previous experiences with some instabilities in their original iterative method and the fact that a least squares solution permits the use of more hydrophones than air guns and various array designs. We have incorporated an additional feature into the bubble motion logic to handle guns fired at different times (e.g. over-under airgun arrays). Reference Ziolkowski, A., Parkes, G., Hatton, L. and Haugland, T., 1982, The signature of an airgun array: Computation from near-field measurements including interaction: Geophysics, 47, 1413-1421.

SOURCE SIGNATURE DECONVOLUTION USING NEAR FIELD HYDROPHONE (NFH) DATA

01. Input(left)andtwozero-phasedanddebubbleddatasets,usingfarfieldsignatures(FFS)derivedfromtheNFHrecordings(right)andasinglemodelledFFS(centre).Each dataset has been bandpass filtered between 1-6 Hz. The water bottom horizon is shown dipping to the left near the top of the section. Accurate zero-phasing is achievedusingtheFFSderivedfromtheNFHdata,butnotwiththemodelledFFS.

INPUT MODELLED FAR FIELD USING NFH DATA

DUG SWAMI is our 3D multiple attenuation algorithm for shallow water settings.

In marine geophysics it is difficult to acquire short offsets. This is a problem in shallow water because the primary reflections, that might normally be used to predict multiples, are missing.

However, given the data (acquired at the available offsets) with multiples, we can invert for a multi-dimensional prediction filter with a wave theoretical pedigree. The filter is designed by minimising the energy in the output of a prediction error process. By use of the focal transform, it is constrained to be composed of a sparse set of hyperbolic functions based upon the wave equation. This filter is an excellent

approximation of the sea-bed reflector and any other reflectors included. Given this filter we may either predict the multiples directly or predict the near offset traces.

If we predict the multiples directly they can be directly subtracted from the data or used as a multiple model in a subtraction scheme. If we predict the near offset traces then the data is perfectly prepared for 3D SRME. In this way longer period free-surface multiples can be attenuated using 3D SRME even on shallow water data.

DUG SWAMI (3D SHALLOW WATER ATTENUATION OF MULTIPLES BY INVERSION)

02. Nearchannelsectionbefore(left)andafter(right)applicationofDUGSWAMI.Timerangeofthedatashownisfrom0-4000ms.

BEFORE DUG SWAMI AFTER DUG SWAMI

DUG IME (INTERBED MULTIPLE ELIMINATION)

03. Stacksectionbefore(top)andafter(middle)DUGIME.Thepredictedinterbedmultiplesareshowninthebottomfigure.Inthiscasethewaterbottomhorizonisactingasthemultiplegenerator.Timerangeofthedatashownisfrom5300-6400ms.(DatacourtesySpectrumGeo)

AFTER DUG IME

INTERBED MULTIPLE MODEL

BEFORE DUG IME

04. Crossline(above)andtimeslice(below)throughamidoffset(1000m)beforeandafter5Dregularisation(DUGReg).

DUG’s multi-dimensional interpolation and regularisation of 3D pre-stack data utilises a projection onto convex sets (POCS) algorithm .

Our approach is based on a time domain, high-resolution, multi-dimensional Radon transform and can be run in 2D, 3D, 4D or 5D. For full/ w ide - a zimuth d ata multi-dimensional regularisation of COV gathers can take place prior to migration using DUG Reg. For conventional binning DUG Reg produces a distinct set of azimuth sector/offset volumes. Fold

coverage in the azimuth sectors can be thoroughly dealt with to produce data with less noise, better continuity and amplitude preservation.

DUG Reg

BEFORE DUG Reg AFTER DUG Reg

BEFORE DUG Reg AFTER DUG Reg

05. Timeslice(400ms)before(left)andafter(right)acquisitionfootprintremoval.

06. Timeslicebefore(left)andafter(right)anisotropicazimuthalmoveoutcorrection.ThisdataisfromAustralia’ssouthernmargin(courtesyOriginEnergy).

DUG PRE-PROCESSING

BEFORE ACQUISITION FOOTPRINT REMOVAL AFTER ACQUISITION FOOTPRINT REMOVAL

BEFORE ANISOTROPIC AZIMUTHAL MOVEOUT AFTER ANISOTROPIC AZIMUTHAL MOVEOUT

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