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Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 111/09/2006
E&P Challenges for imaging
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 211/09/2006
Outline
E&P targets Imaging objectives
Emerging technologies
State of the art imaging workflows
Conclusion
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 311/09/2006
E&P Challenges
Exploration: findings in new - difficult/expensive - areas Deep / Ultra-deep offshore Deep reservoirs (11000m) Sub-salt / sub-basalt Complex geology (foothills, etc.) Onshore reservoirs Heavy oil Tight gas Gas and Oily shales
Production: focus on Improved Oil Recovery (IOR) through better prediction Onshore (current recovery factor » 30%) Low reservoir quality (weak porosity and/or permeability) HP-HT reservoirs Time-lapse seismic Semi-permanent / Permanent / Passive monitoring
Each of these challenges raises specific geophysical issues (S/N, bandwidth, resolution, etc.)
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 411/09/2006
E&P Challenges
Exploration: findings in new - difficult/expensive - areas Deep / Ultra-deep offshore Deep reservoirs (11000m) Sub-salt / sub-basalt Complex geology (foothills, etc.) Onshore reservoirs Heavy oil Tight gas Gas and Oily shales
Production: focus on Improved Oil Recovery (IOR) through better prediction Onshore (current recovery factor » 30%) Low reservoir quality (weak porosity and/or permeability) HP-HT reservoirs Time-lapse seismic Semi-permanent / Permanent / Passive monitoring
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 511/09/2006
Complex subsurface features
Deep
Sharp velocity contrasts
Imaging below high velocity bodies (sub-salt, sub-basalt)
Shallow anomalies (dunes, topography, permafrost, channels)
Fault networks
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 611/09/2006
Where is the salt base?
Arbitrary 2D line through large 3D PSDM volume – Close-Up
?
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 711/09/2006
Imaging issues
Illumination
Resolution (sampling, frequency content)
De-noising (migration, multiples,…)
Focusing (velocity model building)
Positioning (anisotropy)
Amplitude preservation (repeatability, seismic attributes)
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 811/09/2006
Imaging issues
Illumination
Resolution (sampling, frequency content)
De-noising (migration, multiples,…)
Focusing (velocity model building)
Positioning (anisotropy)
Amplitude preservation (repeatability, seismic attributes)
Land and Marineacquisitiongeometries
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 911/09/2006
Minimize cost: Cost per trace must drop to improve survey characteristics for the same price
Increase the value of seismic for E&P
Reduce turnaround time: Seismic interpretation in time for drilling
Improve quality: Better seismic imaging: - enhanced resolution (vertical) - quantitative reservoir attributes
from Veritas
Adapt to any environment: Equipment to support efficient acquisition in all conditions
from BGP
Seismic acquisition needs
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 1011/09/2006
More flexibility:
Deploy a seamless spread
whatever the terrain conditions
More channels:
Large spread
with dense RP sampling
High channel count crew
More records:
Increase source productivity,
SP density
Vibrator array , Sinopec, Saudi Arabia
Increase quality & productivity
Seismic acquisition answers
From Sercel
From Sercel
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 1111/09/2006
Wide-azimuth 3D or “3D2”
Current 3D geometry 3D2 geometry
Basic 3D3D22 parameters for dense acquisition: 12.5*12.5 m² bin size, four times more bins than current geometries. Very high trace density, ten times more than current with folds over 1000. Isotropic illumination at all offsets
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 1211/09/2006
Receiver arrays, today
A 25-m group interval provides adequate signal sampling at frequencies up to 100 Hz for 95 % of reservoirs (>1000 m).
Why receiver arrays of limited size?
Although 25-m arrays do not significantly affect signal amplitudes at frequencies up to 100 Hz, they are needed to correctly sample surface waves and bring ambient noise to acceptable levels.
25 m
5 to 25 m
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 1311/09/2006
Examples of land 3D2 geometry
20 000-channel crew with 14 400 live
12.5 x 12.5 m² m² bin size
6*6 km²
60 lines of 240 channels
x: 25m y: 100m
Centered shot point lines
Medium depth reservoir
6*12 km²
30 lines of 480 channels
x: 25m y: 200m
2 external shot point lines
Deep reservoir
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 1411/09/2006
A vibrator group sweeps without waiting for the
previous group’s sweep to terminate
Continuous recording of
composite signal
Slip time
=
Time shift between sweeps
Correlation by reference
signal
Slip time
Tim
e
PDO Vibroseis Technique
More records: slip sweep principle
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 1511/09/2006
Fund H2 H3 H4 Fund + Harm
Fund H2 H3 H4 Fund + Harm
0
Effect of correlation on harmonic noise
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 1611/09/2006
Post slip-sweep processing
HPVA™HPVA™ technology for
estimating the
harmonic content of
each vibro-seis VP
recorded with the slip-
sweep method, and
then subtracting the
predicted noise from
the field data.
Reference Slip-sweep HPVAHPVA
From CGG
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 1711/09/2006
Surface noisepreservedattenuated
Point acquisition
SignalHF preservedHF attenuated
-- No source noise attenuation-- No ambient noise attenuation- Good coupling mandatory - Less sensitivity per RP
-- Noise often aliased-- HF filtering (intra array statics)- Azimuth dependant filtering
During
acquisition++ Source noise attenuation+ Ambient noise attenuation+ Trace-to-trace coupling stabilized+ Transmission power & sensitivity
++ Noise well sampled++ HF content preserved+ Isotropic recording
Array acquisition
Point acquisition principle
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 1811/09/2006
Marine systems
From D.Monk – Apache
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 1911/09/2006
Why do we need wide-azimuth?
Limitations of conventional 3D marine acquisition
Single or, at most, narrow azimuth
Not symmetrically sampled in the in-line and cross-
line directions
Unbalanced receiver and shot densities
Hence, the wave-field is not fully sampled
As a result, complex structures are not optimally imaged with conventional 3D
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 2011/09/2006
What do we want ?
Isotropic distribution of source-receiver pairs
Full target illumination: in complex areas, among a high multiplicity of rays some will carry the useful signal
Proper input to tomography: optimal angular illumination redundancy
Improved imaging of multi-azimuth dipping structures
Extraction of rock elastic properties including fracture
Isotropic wave-field sampling in the receiver and shot-point domains:
Isotropic (3D) FK/Radon filtering of unwanted waves
3D Surface Multiple Attenuation
Wave-equation imaging - 3D pre-stack datuming
3D Surface scattered noise attenuation (during the stacking or
focussing process)
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 2111/09/2006
Limitations of conventional acquisition
In line / Cross line ratio is 6 to 20
Current algorithms and workflows adapted / designed
for almost single-azimuth acquisitions
In line: 9000m Cross line: 450m
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 2211/09/2006
Conventional perpendicular acquisitions
Courtesy of BP
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 2311/09/2006
Wide-azimuth streamer acquisitions
New algorithms and workflows are needed to
take full advantage of new acquisition patterns
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 2411/09/2006
OPERATIONAL SET-UP
Wide-azimuth: means
One 10-streamer vessel, 90 km
9000m
900m
One or more dual source vessels
+
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 2511/09/2006
Wide-azimuth: methodology
18,000 metres
Receiver template
5,000 metres
Super Shot Point Gather50 Receiver Lines
The super shot pattern is repeated in the cross-line direction to build the total CMP fold of multiplicity.
The fold will depend on the nominal distance between source lines.
Y + 1000 m
Y + 2000 m
Y + 3000 m
Y + 4000 m
Y + 5000 m
Y + 1000 m
Y + 2000 m
Y + 3000 m
Y + 4000 m
Y + 5000 m
1 2
3 4
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 2611/09/2006
Comparing narrow and wide-azimuth
Conventional geometry Full fold area 1000 sq.km.
One recording & source vessel
Receiver spread 10 x 9000 m = 9
sq.km.
Fold 80
Azimuths +/- 3 degrees
Infill level 33 % (optimistic)
Base duration 34 days
Base cost 1
Wide-azimuth geometry Full fold area 1000 sq.km.
One recording + two source vs.
Receiver spread 50 x 2 x 9000 m =
90 sq.km.
Fold 40 x 2 x 5 = 400
Azimuths +/- 90 degrees
Infill level 0 % (assumption)
Duration 129 days ~ x 4
Estimated cost ~ x 6 / 6.5
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 2711/09/2006
from M. Lansley, PGS Onshore, The Leading Edge, October 2004
2003 VibroSeis 3D HD data 663,552 traces per mile2
Bin size 55 x 55 ft, Fold 72
1995 3D Explosives data36,864 traces per mile2
Bin size 165 x 110 ft, Fold 24
More channels for higher resolution
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 2811/09/2006
VARG: base Cretaceous amplitude map
VARG-2002: all three shooting directions
Previous PSDM of ST8802: E-W shooting direction
from S. Hegna and D. Gaus, PGS Geophysical, EAGE, 2003
Wide azimuth for higher resolution
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 2911/09/2006
Offset sorted CMP
Azimuth sorted CMP
from M. Williams & E. JennerThe Leading Edge, Aug.02
Near Far North South North
Anisotropy is detected by
wide azimuthal distribution
and can be compensated for
From this compensation
we get better imaging and
info about fracture
orientation & density
Wide azimuth for detecting anisotropy
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 3011/09/2006
Sources with broad signal bandwidth
Lower frequencies for deeper targets Limitations are more on the source side
Piezoelectric hydrophones in marine down to 0.5Hz (wave height measure helps de-ghosting)
MEMS based digital accelerometers in land (linear response on 0-800Hz)
Land sources Longer explosions improves conversion into elastic wave and
broadens bandwidth with pick amplitude shifted towards lower frequencies
Vibrators with bigger mass broadens bandwidth broadened at both extremities (5-250Hz) and allows point source
Marine sources Air guns: bandwidth driven by bubble period and depth (ghost).
low frequency (~5Hz)
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 3111/09/2006
New acquisition geometries
More channels and shots (higher fold, dense and regular sampling wave-field sampling)
Wide azimuth (isotropic wave-field sampling)
Broader signal bandwidth and lower frequencies (resolution)
What about global offsets? near future is 2D
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 3211/09/2006
Imaging issues
Illumination
Resolution (sampling, frequency content)
De-noising (migration, multiples,…)
Focusing (velocity model building)
Positioning (anisotropy)
Amplitude preservation (repeatability, seismic attributes)
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 3311/09/2006
Move away from offset cube pre-processing and azimuth sector processing
One-pass solutions preferably on shot gathers De-noising and preserving low frequencies De-aliasing Surface-related multiple attenuation Internal multiple attenuation
Wide-azimuth seismic processing
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 3411/09/2006
WE based methodWE based method
One-way wave-equation migration of p-gathers
Mean semblance maximization.No picking,
P-gather file
Wave-equation
Focusing: state of the art & near future
Velocity model building is a three stage iterative process
11st st generation generation tomographytomography
Kirchhoff migration
Manual RMO picking, Sparse
xn, yn, zn, cn
Ray-based tomography
22ndnd generation generation tomographytomography
Kirchhoff migration
Automatic RMO picking,Dense
xn, yn, zn, {cni}i
Ray-based tomography
Migration
Commonimage gathers
Errormeasurement
Velocity update
Velocity model
Error file
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 3511/09/2006
Simple view of underdetermined problem
narrow azimuth angular redundancy (= 45°)
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 3611/09/2006
wide azimuth angular redundancy
Simple view of underdetermined problem
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 3711/09/2006
Medical tomography angular redundancy
Simple view of underdetermined problem
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 3811/09/2006
Building a linear equation
Velocity parameter
Simple view of underdetermined problem
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 3911/09/2006
Building a linear equation
Simple view of underdetermined problem
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 4011/09/2006
Medical tomography
Simple view of underdetermined problem
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 4111/09/2006
Ray-based VMB: GoM example
CIP GatherConditioning
Dense VolumetricRMO picking
3D Geostatisticalfiltering of RMO
Volumetric DipComputation
RMO High-Grading
3D DepthTomography
Inversion QC
Automated
Batch
Processes
Example of workflow
designed for Gulf of Mexico
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 4211/09/2006
Ray-based VMB: observable quantities
CIP Gather RMO
For each locally coherent event
X, Y
Z
Offset,
Zx,y (h)
CIP Gather
Image Stack DipX, Y
Z
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 4311/09/2006
Ray-based VMB: Data Conditioning
CIP GatherConditioning
Dense VolumetricRMO picking
3D Geostatisticalfiltering of RMO
Volumetric DipComputation
RMO High-Grading
3D DepthTomography
Inversion QC
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 4411/09/2006
Ray-based VMB: volumetric skeleton
CIP GatherConditioning
Dense VolumetricRMO picking
3D Geostatisticalfiltering of RMO
Volumetric DipComputation
RMO High-Grading
3D DepthTomography
Inversion QC
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 4511/09/2006
Ray-based VMB: automated RMO pick.
CIP GatherConditioning
Dense VolumetricRMO picking
3D Geostatisticalfiltering of RMO
Volumetric DipComputation
RMO High-Grading
3D DepthTomography
Inversion QC
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 4611/09/2006
Ray-based VMB: RMO pick filtering
CIP GatherConditioning
Dense VolumetricRMO picking
3D Geostatisticalfiltering of RMO
Volumetric DipComputation
RMO High-Grading
3D DepthTomography
Inversion QC
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 4711/09/2006
Ray-based VMB: volumetric dip picking
CIP GatherConditioning
Dense VolumetricRMO picking
3D Geostatisticalfiltering of RMO
Volumetric DipComputation
RMO High-Grading
3D DepthTomography
Inversion QC
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 4811/09/2006
Ray-based VMB: RMO High-Grading
Local semblance (RMO)
Sigma cube
Structural semblance
CIP GatherConditioning
Dense VolumetricRMO picking
3D Geostatisticalfiltering of RMO
Volumetric DipComputation
RMO High-Grading
3D DepthTomography
Inversion QC
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 4911/09/2006
0
4
8
Dep
th (
km)
Ray-based VMB: VelTracer™ 3D tomo
3D Finite Offset Tomographic Inversion
True 3D Depth Tomography using ray tracing
All of the RMO information used (full offset)
Non-linear Inversion
TTI capable
64-bit multi-CPU Linux implementation
1 Pass !
CIP GatherConditioning
Dense VolumetricRMO picking
3D Geostatisticalfiltering of RMO
Volumetric DipComputation
RMO High-Grading
3D DepthTomography
Inversion QC
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 5011/09/2006
Ray-based VMB: VelTracer™ 3D tomo
kinematicde-migration
initialPreSDM
RMO picking(OFFSET, RMO, DIP)
invariantsTIMESR GRADTSR
OFFSETSR AZIMUTHSR
invariantsTIMESR GRADTSR
OFFSETSR AZIMUTHSR
predicted RMO
Update of velocity
kinematicRe-migration
finalPreSDM
Y
non-linearnon-linearupdateupdate
looploopN
RMOminimized?
VelTracer Internal Workflow
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 5111/09/2006
Ray-based VMB: VelTracer™ kinematics
•RMO Pick Z(h)•Dip
Src (T, Tg) Rec (T, Tg)(Offset, Azimuth)CIP
Demigration to surface consistent
invariants
Update velocity by
linear Inversion
•New depth point•Predicted RMO
Re-migration to predict RMO
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 5211/09/2006
Ray-based VMB: VelTracer™ criterion
misaligned image facets
x,y
z
CIP gatherRMO picks
z
h
RMO corresponds to spatial
misalignment of image facets for a
local reflector
z
x,y
Maximum alignment of adjacent facets for local reflector
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 5311/09/2006
• QC
• Predicted RMO attribute
• Gamma attribute
• Velocity
• CIP Gather flatness – actual RMO
• Pre-SDM Image
Ray-based VMB: Inversion QC
CIP GatherConditioning
Dense VolumetricRMO picking
3D Geostatisticalfiltering of RMO
Volumetric DipComputation
RMO High-Grading
3D DepthTomography
Inversion QC
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 5411/09/2006
Ray-based VMB: RMO and gamma
GAMMA – Initial ModelRMO – Initial Model GAMMA – Updated ModelRMO – Updated Model
RMO (m)
Gamma
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 5511/09/2006
Ray-based VMB: CIP Gather Flatness
Pre-SDM – Initial ModelPreSDM – updated model
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 5611/09/2006
Ray-based VMB: sediment velocity
Velocity – Initial ModelPre-SDM – Initial Model Velocity – updated modelPreSDM – updated model
salt mask
10 km
0 km
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 5711/09/2006
12 km
0 km
Ray-based VMB: Velocity & PreSDM
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 5811/09/2006
Marmousi using one-way WE migration
Synthetic dataset recomputed using one-way wave-equation
Realistic geometry and signal bandwidth: 240 shots 96 receivers per shot, streamer geometry, max offset
2.4 km Frequency range: fmin = 5 Hz, fmax = 40 Hz
Initial model: 1D, linear gradient 1500 m/s 3000 m/s
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 5911/09/2006
initial velocity model
Marmousi using one-way WE migration
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 6011/09/2006
final velocity model
Marmousi using one-way WE migration
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 6111/09/2006
exact velocity model
Marmousi using one-way WE migration
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 6211/09/2006
final velocity model
Marmousi using one-way WE migration
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 6311/09/2006
initial migration
Marmousi using one-way WE migration
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 6411/09/2006
final migration
Marmousi using one-way WE migration
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 6511/09/2006
exact migration
Marmousi using one-way WE migration
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 6611/09/2006
initial CIP p-gathers
Marmousi using one-way WE migration
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 6711/09/2006
final CIP p-gathers
Marmousi using one-way WE migration
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 6811/09/2006
exact CIP p-gathers
Marmousi using one-way WE migration
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 6911/09/2006
Marmousi using waveform Inversion
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 7011/09/2006
Marmousi using waveform Inversion
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 7111/09/2006
Marmousi using waveform Inversion
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 7211/09/2006
Marmousi using waveform Inversion
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 7311/09/2006
1 – 7 Hz of data!!1 – 7 Hz of data!!exact modelexact model
BP model using waveform Inversion
from G.Pratt
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 7411/09/2006
Imaging issues
Illumination
Resolution (sampling, frequency content)
De-noising (migration, multiples,…)
Focusing (velocity model building)
Positioning (anisotropy)
Amplitude preservation (repeatability, seismic attributes)
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 7511/09/2006
TTI velocity model building
VTI Anisotropy TTI Anisotropy
Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 7611/09/2006
Fast salt body delineation
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Fast salt body delineation
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Fast salt body delineation
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Imaging issues
Illumination
Resolution (sampling, frequency content)
De-noising (migration, multiples,…)
Focusing (velocity model building)
Positioning (anisotropy)
Amplitude preservation (repeatability, seismic attributes)
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PSDM structural data PSDM 4D difference
New OWC interpretation (blue)Top reservoir interpretation (green)
Remaining oil column
Figure 1: Seismic cross section of the Statfjord reservoir, PSDM structural cube on the left and PSDM 4D difference cube on the right. The top reservoir (green) is interpreted on the structural cube, while the new OWC (blue) is interpreted on the 4D difference cube.
The remaining oil column (attic oil) is the difference between the two interpretations.
Time-lapse in depth
from Norsk Hydro and CGG
Structure (PreSDM) 4D difference (PreSDM)
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Conclusion
Incremental improvement of imaging through:
Denser acquisition geometry
Wide azimuth geometry
Broader signal bandwidth
One-way WE-based velocity model building
3D Global offsets, Full waveform inversion
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Acknowledgment
Many thanks to Denis Mougenot, Volker Dirks, Gilles Lambaré, Robert Soubaras, Bruno Gratacos, Serge Zimine, Jean-Jacques Postel, Michel Manin and Robert Taylor who helped me prepare this presentation
CGG for giving permission to show data
WesternGeco, Veritas DGC, PGS, Sercel, Liaohe Geophysical and BashNeftGeofisika for used images from WEB-site / litterature
Dave Monk (Apache Corp.), Williams & E. Jenner for use of published images
BP for data used by EAGE 2005 depth imaging benchmark