11/09/2006 Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 1 E&P Challenges for imaging.

Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 111/09/2006

E&P Challenges for imaging


Outline

E&P targets Imaging objectives

Emerging technologies

State of the art imaging workflows

Conclusion


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.)


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


Complex subsurface features

Deep

Sharp velocity contrasts

Imaging below high velocity bodies (sub-salt, sub-basalt)

Shallow anomalies (dunes, topography, permafrost, channels)

Fault networks


Where is the salt base?

Arbitrary 2D line through large 3D PSDM volume – Close-Up

?


Imaging issues

Illumination

Resolution (sampling, frequency content)

De-noising (migration, multiples,…)

Focusing (velocity model building)

Positioning (anisotropy)

Amplitude preservation (repeatability, seismic attributes)


Imaging issues

Illumination






Land and Marineacquisitiongeometries


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


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


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


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


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


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


Fund H2 H3 H4 Fund + Harm

Fund H2 H3 H4 Fund + Harm

0

Effect of correlation on harmonic noise


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


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


Marine systems

From D.Monk – Apache


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


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)


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


Conventional perpendicular acquisitions

Courtesy of BP


Wide-azimuth streamer acquisitions

New algorithms and workflows are needed to

take full advantage of new acquisition patterns


OPERATIONAL SET-UP

Wide-azimuth: means

One 10-streamer vessel, 90 km

9000m

900m

One or more dual source vessels

+


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


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


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


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


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


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)


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


Imaging issues

Illumination







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


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


Simple view of underdetermined problem

narrow azimuth angular redundancy (= 45°)


wide azimuth angular redundancy



Medical tomography angular redundancy



Building a linear equation

Velocity parameter



Building a linear equation



Medical tomography



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


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


Ray-based VMB: Data Conditioning





RMO High-Grading

3D DepthTomography

Inversion QC


Ray-based VMB: volumetric skeleton





RMO High-Grading

3D DepthTomography

Inversion QC


Ray-based VMB: automated RMO pick.





RMO High-Grading

3D DepthTomography

Inversion QC


Ray-based VMB: RMO pick filtering





RMO High-Grading

3D DepthTomography

Inversion QC


Ray-based VMB: volumetric dip picking





RMO High-Grading

3D DepthTomography

Inversion QC


Ray-based VMB: RMO High-Grading

Local semblance (RMO)

Sigma cube

Structural semblance





RMO High-Grading

3D DepthTomography

Inversion QC


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 !





RMO High-Grading

3D DepthTomography

Inversion QC


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


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


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


• QC

• Predicted RMO attribute

• Gamma attribute

• Velocity

• CIP Gather flatness – actual RMO

• Pre-SDM Image

Ray-based VMB: Inversion QC





RMO High-Grading

3D DepthTomography

Inversion QC


Ray-based VMB: RMO and gamma

GAMMA – Initial ModelRMO – Initial Model GAMMA – Updated ModelRMO – Updated Model

RMO (m)

Gamma


Ray-based VMB: CIP Gather Flatness

Pre-SDM – Initial ModelPreSDM – updated model


Ray-based VMB: sediment velocity

Velocity – Initial ModelPre-SDM – Initial Model Velocity – updated modelPreSDM – updated model

salt mask

10 km

0 km


12 km

0 km

Ray-based VMB: Velocity & PreSDM


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


initial velocity model



final velocity model



exact velocity model



final velocity model



initial migration



final migration



exact migration



initial CIP p-gathers



final CIP p-gathers



exact CIP p-gathers



Marmousi using waveform Inversion


1 – 7 Hz of data!!1 – 7 Hz of data!!exact modelexact model

BP model using waveform Inversion

from G.Pratt


Imaging issues

Illumination







TTI velocity model building

VTI Anisotropy TTI Anisotropy


Fast salt body delineation


Imaging issues

Illumination







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)


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


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

11/09/2006 Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 1 E&P Challenges for imaging.

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Transcript of 11/09/2006 Ecole thématique CNRS-UNSA-CGG-SEISCOPE 2006 1 E&P Challenges for imaging.