imaging and full waveform inversion of seismic data from ... imaging and full waveform inversion of...
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![Page 1: Imaging and Full Waveform Inversion of seismic data from ... Imaging and Full Waveform Inversion of seismic data from the CO 2 gas cloud at Sleipner Espen Birger Raknes, B˝rge Arntsen,](https://reader035.vdocuments.mx/reader035/viewer/2022062508/60472320daac6b23e5374f0f/html5/thumbnails/1.jpg)
Theory Results Conclusions
Imaging and Full Waveform Inversion ofseismic data from the CO2 gas cloud at
Sleipner
Espen Birger Raknes, Børge Arntsen, and Wiktor Weibull
Norwegian University of Science and Technology (NTNU)Department of Petroleum Engineering & Applied Geophysics
E-mail: [email protected]
ROSE Meeting 2015April 28th 2015
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Theory Results Conclusions
Background
• The applications of full waveform inversion (FWI) onsynthetic and field data the last decade have proved thatFWI is a promising method for parameter model estimation
• The increase in computational power leads to an increasein possible problem sizes and type of wave phenomenaincluded in the modeling and inversion
• Limited number of 3D applications in the literature
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Theory Results Conclusions
Objectives
• Apply three dimensional elastic isotropic FWI on fieldtime-lapse data from the Sleipner area
• Use the inverted elastic models to obtain depth migrationseismic images of the area before and after ten years ofinjection of CO2.
• Investigate the migration path for the injected CO2 gas.
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Theory Results Conclusions
Outline
• Theory• Full waveform inversion• Imaging work flow
• Results• Baseline inversion• Monitor inversion• Seismic images
• Conclusions
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Theory Results Conclusions
A quick overview of full waveform inversion
Overall goal
Find a parameter model from which it is possible to createsynthetic data that is close to some measured data
Define S(m) as the measure between synthetic and measureddata. The FWI is then the problem
arg minm
S(m)
Solved using an iterative method
mk+1 = mk − αkH−1k gk,
mk model at iteration kgk gradient of S(m) at iteration kHk Hessian of S(m) at iteration kαk step length at iteration k
Start point
End point
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Theory Results Conclusions
A quick overview of full waveform inversion
Overall goal
Find a parameter model from which it is possible to createsynthetic data that is close to some measured data
Define S(m) as the measure between synthetic and measureddata. The FWI is then the problem
arg minm
S(m)
Solved using an iterative method
mk+1 = mk − αkH−1k gk,
mk model at iteration kgk gradient of S(m) at iteration kHk Hessian of S(m) at iteration kαk step length at iteration k
Start point
End point
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Theory Results Conclusions
Schematic view of FWI
Initial
modelModeling
Are synthetic
and real data
close enough?
End
model
Gradient
calculation
New model
In parallelSynchronization
yes no
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Theory Results Conclusions
Time-lapse full waveform inversion
Goal
Use full waveform inversion to quantify changes in time for
parameters affecting wave propagation.
May be used
• as monitoring tool during the life-time of a reservoir
• to monitor injection of CO2 in CCS experiments
• quantify amount of injected CO2
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Theory Results Conclusions
Time-lapse full waveform inversion
Goal
Use full waveform inversion to quantify changes in time for
parameters affecting wave propagation.
Challenges
• Need to perform at least two inversions
• The method may introduce artifacts in the time-lapseimages due to for instance
• non-linearity• ill-posedness• data differences• bad repeatability in the time-lapse data
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Theory Results Conclusions
Seismic imaging work flow
Initial modelBaseline data
Baseline FWI
Baseline model Depth migrationMonitor data
Monitor FWI
Monitor model Depth migration
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Theory Results Conclusions
Seismic imaging work flow
Initial modelBaseline data
Baseline FWI
Baseline model Depth migration
Monitor data
Monitor FWI
Monitor model Depth migration
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Theory Results Conclusions
Seismic imaging work flow
Initial modelBaseline data
Baseline FWI
Baseline model Depth migrationMonitor data
Monitor FWI
Monitor model Depth migration
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Theory Results Conclusions
Seismic imaging work flow
Initial modelBaseline data
Baseline FWI
Baseline model Depth migrationMonitor data
Monitor FWI
Monitor model Depth migration
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Theory Results Conclusions
Results: Sleipner data details• Baseline dataset: 1994 survey
• 852 shots, 570840 data traces• 1700 m offset
• Monitor dataset: 2006 survey• 1180 shots, 1274400 data traces• 1700 m offset
0 500 1000 1500 2000 2500 3000X (m)
0
1000
2000
3000
4000
5000
6000
7000
8000
Y (m
)
0
20
40
60
80
100
120
140
160
180
# of CMPs
0 500 1000 1500 2000 2500 3000X (m)
0
1000
2000
3000
4000
5000
6000
7000
8000
Y (m
)
0
25
50
75
100
125
150
175
200
225
250
# of CMPs
Fold maps: left: baseline, right: monitor.
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Theory Results Conclusions
Inversion strategy
• Invert for vp, and couple vs and ρ using empirical
relationships
• Invert sequentially using the frequency bands: 6–8Hz,
6–11Hz, 6–15Hz.
• Source signatures are estimated using FWI
• First, invert for baseline data to obtain a baseline elastic
model
• Second, use target-oriented FWI when inverting for the
monitor data
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Theory Results Conclusions
Estimated source signatures
0.0 0.2 0.4 0.6 0.8 1.0Time (s)
0.0 0.2 0.4 0.6 0.8 1.0Time (s)
0.0 0.2 0.4 0.6 0.8 1.0Time (s)
0.0 0.2 0.4 0.6 0.8 1.0Time (s)
0.0 0.2 0.4 0.6 0.8 1.0Time (s)
0.0 0.2 0.4 0.6 0.8 1.0Time (s)
Source signatures: top: baseline dataset, bottom: monitor dataset.
Left: 6–8 Hz, middle 6–11 Hz, right: 6–15 Hz.
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Theory Results Conclusions
Baseline FWI: Vertical slice
2500 3500 4500 5500y (m)
200400600800100012001400
z (m
)
1500 1700 1900 2100 2300vp (m/s)
Initial model
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Theory Results Conclusions
Baseline FWI: Vertical slice
2500 3500 4500 5500y (m)
200400600800100012001400
z (m
)
1500 1700 1900 2100 2300vp (m/s)
Final model
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Theory Results Conclusions
Baseline FWI: Data
−1.0 −0.5 0.0 0.5 1.0Amplitude
0.0
0.5
1.0
1.5
2.0
Tim
e (s
)
InitialFinalField
−1.0 −0.5 0.0 0.5 1.0Amplitude
0.0
0.5
1.0
1.5
2.0
Tim
e (s
)
InitialFinalField
−1.0 −0.5 0.0 0.5 1.0Amplitude
0.0
0.5
1.0
1.5
2.0
Tim
e (s
)
InitialFinalField
Comparison: left: 6–8 Hz, middle: 6–11 Hz, right: 6–15 Hz.
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Theory Results Conclusions
Baseline migration: Vertical slice
2500 3500 4500 5500y (m)
200
400
600
800
1000
1200
1400
z (m
)
Initial model
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Theory Results Conclusions
Baseline migration: Vertical slice
2500 3500 4500 5500y (m)
200
400
600
800
1000
1200
1400
z (m
)
Final inverted model
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Theory Results Conclusions
Baseline FWI: Horizontal slice
400 900 1400 1900x (m)
2200
2900
3600
4300
5000
5700
y (m
)
1700 1800 1900 2000vp (m/s)
400 900 1400 1900x (m)
2200
2900
3600
4300
5000
5700
y (m
)
1700 1800 1900 2000vp (m/s)
vp: left: initial, right: final.
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Theory Results Conclusions
Baseline FWI: Horizontal slice
400 900 1400 1900x (m)
2200
2900
3600
4300
5000
5700
y (m
)
400 900 1400 1900x (m)
2200
2900
3600
4300
5000
5700
y (m
)
Initial: left: seismic image, right: overlay.
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Theory Results Conclusions
Baseline FWI: Horizontal slice
400 900 1400 1900x (m)
2200
2900
3600
4300
5000
5700
y (m
)
400 900 1400 1900x (m)
2200
2900
3600
4300
5000
5700
y (m
)
Final: left: seismic image, right: overlay.
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Theory Results Conclusions
Monitor FWI: Vertical slice
2500 4000 5500y (m)
200
600
1000
1400
z (m
)
1500 1750 2000 2250 2500vp (m/s)
Baseline
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Theory Results Conclusions
Monitor FWI: Vertical slice
2500 4000 5500y (m)
200
600
1000
1400
z (m
)
1500 1750 2000 2250 2500vp (m/s)
Monitor
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Theory Results Conclusions
Monitor FWI: Vertical slice
2500 4000 5500y (m)
200
600
1000
1400
z (m
)
−400 −200 0 200 400vp (m/s)
Time-lapse
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Theory Results Conclusions
Migration: Vertical slice
3500 4000 4500 5000y (m)
700
800
900
1000
1100
z (m
)
3500 4000 4500 5000y (m)
700
800
900
1000
1100
z (m
)
Close-up of gas cloud: top: baseline, bottom: monitor.
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Theory Results Conclusions
Migration: Horizontal slice
750 1250 1750x (m)
2500
3000
3500
4000
4500
5000
5500
y (m
)
750 1250 1750x (m)
2500
3000
3500
4000
4500
5000
5500
y (m
)
750 1250 1750x (m)
2500
3000
3500
4000
4500
5000
5500
y (m
)Slice at z = 881.25m: left: baseline, middle: monitor, right: overlay
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Theory Results Conclusions
Migration: Horizontal slice
750 1250 1750x (m)
2500
3000
3500
4000
4500
5000
5500
y (m
)
750 1250 1750x (m)
2500
3000
3500
4000
4500
5000
5500
y (m
)
750 1250 1750x (m)
2500
3000
3500
4000
4500
5000
5500
y (m
)Slice at z = 918.75m: left: baseline, middle: monitor, right: overlay
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Theory Results Conclusions
Migration: Horizontal slice
750 1250 1750x (m)
2500
3000
3500
4000
4500
5000
5500
y (m
)
750 1250 1750x (m)
2500
3000
3500
4000
4500
5000
5500
y (m
)
750 1250 1750x (m)
2500
3000
3500
4000
4500
5000
5500
y (m
)Slice at z = 943.75m: left: baseline, middle: monitor, right: overlay
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Theory Results Conclusions
Migration: Different migration models
750 1250 1750x (m)
2500
3000
3500
4000
4500
5000
5500y (m
)
750 1250 1750x (m)
2500
3000
3500
4000
4500
5000
5500
y (m
)
Slice at z = 918.75m: left: baseline FWI model, right: monitor FWI model
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Theory Results Conclusions
Conclusions
• FWI improves the elastic models and matches the field data
• FWI produces models that can improve the resolution and
focusing of seismic images
• Injected gas at Sleipner has migrated into structures that
are visible on the baseline images
• The gas has migrated upwards through a fault
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Theory Results Conclusions
Acknowledgements
We thank the BIGCCS centre and the ROSE consortium for
financing this research.