common reflection surface theory and worldwide data...
TRANSCRIPT
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Common Reflection Surface theoryand worldwide data examples
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Data driven approach
• stacking parameters are determined from data
• local parameter search at each point of stack
• selection of parameter by coherency measures
along stacking surfaces
Common Reflection Surface (CRS) processing
CRS Basics
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CMP Processing
(Mayne, 1962)
CRS Basics – Generalization of the subsurface model
DMO Processing
(Hale, 1991)
CRS Processing
(Hubral, 1999)
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0CMP0S 0G
h
CRS Basics – Generalization of the subsurface model
1 Parameter : VNMO
( )2
22
0
2 4
NMOv
htht +=
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NIP
CRS Basics – Generalization of the subsurface model
CMP
CMP
NIP = normal incident point
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NIP NIP
NIP Wave Normal Wave ( or N Wave )
CMP CMP
CRS Basics – Generalization of the subsurface model
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NIP NIP
KNIP
KN
CRS Basics – Generalization of the subsurface model
CMP CMP
NIPRNR
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NMO versus CRS
NMO Model
CRS Model
NMO Traveltime
CRS Traveltime
1 Parameter : VNMO
( )2
22
0
2 4
NMOv
htht +=
+
+
+=
NIPNR
h
R
x
v
tx
vtt
22
0
2
0
2
0
0
2 cos2sin2
3 Parameters: α, RNIP, RN
CRS Basics – Generalization of the subsurface model
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CRS midpoint aperture
CRS Basics – Generalization of the subsurface model
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2D: 7 x 60 = 420 traces
3D: 7 x 7 x 60 = 2940 traces
CRS Basics – Generalization of the subsurface model
CRS midpoint aperture
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(Hubral et al. 1999)
CRSNMO/DMO
Fit of stacking surfaces / reflection time surfaces
CRS Basics – Generalization of the subsurface model
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Expected advantages of CRS stacking
• Improved signal-to-noise ratio
• Improved imaging of dipping reflections
• Improved imaging in low fold zones
• More detailed velocity model information
CRS Basics – Generalization of the subsurface model
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Alps/Germany
Andes Foreland/Bolivia
Carpathian Mountains/ Poland
Caucasus Mountains/ Russia
Kurdistan/ Irak
Himalaya Foreland/ India
Pyrenees/ Spain
Rockies/ USA
Zagros Mountains/ Iran
CRS in Complex Tectonic Settings
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Colombia 3rd party PreSTM
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Colombia CRS PreSTM
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Colombia 2D 3rd party PreSTM
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Colombia 2D TEEC CRS PreSTM
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Northern Calcareous Alps
Client Time Migration with conventional processing
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Northern Calcareous Alps
TEEC Time Migration with CRS processing
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North Sea
Gulf of Mexico
Persian Gulf
South China Sea
Carribean Sea
Barent Sea
Black Sea
Offshore West Africa
Offshore Nova Scotia
Brasil
CRS on marine data
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Land/TZ/offshore
• Sources:
• Explosives in black
• Airguns in red
• Receiver:
• Receiver in brown
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CMP gathers before CRS processing
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CRS gathers
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PreSTM using CMP gathers
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PreSTM using CRS gather
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Timeslice 1032 ms of PreSTM
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TZ 1032 ms of CRS PreSTM
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1 vessel with2 airguns8 streamer
2 vessels, each with2 airguns2 streamer
1 vessel with2 airguns6 streamer
1 vessel with2 airguns4 streamer
North Sea merge of 4 different acquisition geometries
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1. source/receiver static applied2. True amplitude recovery3. Low cut filter4. Source signature deconvolution5. Debubble6. Linear denoise7. Tau-P deconvolution8. Amplitude adjustment9. Match Filter application10. Radon Demultiple11. Merge of all surveys
Inline of CMP stack
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1. source/receiver static applied2. True amplitude recovery3. Low cut filter4. Source signature deconvolution5. Debubble6. Linear denoise7. Tau-P deconvolution8. Amplitude adjustment9. Match Filter application10. Radon Demultiple11. Merge of all surveys12. CRS processing
Inline of CRS stack
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1. source/receiver static applied2. True amplitude recovery3. Low cut filter4. Source signature deconvolution5. Debubble6. Linear denoise7. Tau-P deconvolution8. Amplitude adjustment9. Match Filter application10. Radon Demultiple11. Merge of all surveys
Time slice at 2100 ms of CMP stack
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1. source/receiver static applied2. True amplitude recovery3. Low cut filter4. Source signature deconvolution5. Debubble6. Linear denoise7. Tau-P deconvolution8. Amplitude adjustment9. Match Filter application10. Radon Demultiple11. Merge of all surveys12. CRS processing
Time slice at 2100 ms of CRS stack
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Inline of vintage PreSTM result
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Inline of CRS PreSTM