1
Fast 3D Target-Oriented Reverse Time Datuming
Shuqian Dong
University of Utah2 Oct. 2008
2
OutlineOutline
• MotivationMotivation
• TheoryTheory
• ConclusionsConclusions
• Numerical TestsNumerical Tests 2-D SEG/EAGE salt model2-D SEG/EAGE salt model
3-D SEG/EAGE salt model3-D SEG/EAGE salt model
3-D field data3-D field data
Motivation Theory Numerical Tests Conclusions
3
OutlineOutline
• MotivationMotivation
• TheoryTheory
• ConclusionsConclusions
• Numerical TestsNumerical Tests 2-D SEG/EAGE salt model2-D SEG/EAGE salt model
3-D SEG/EAGE salt model3-D SEG/EAGE salt model
3-D field data3-D field data
Motivation Theory Numerical Tests Conclusions
4Motivation Theory Numerical Tests Conclusions
z (k
m)
z (k
m)
00
2.02.0
KM image
x (km)x (km)00 8.08.0
Tim
e (s
)T
ime
(s)
00
4.04.0
Common shot gather
x (km)x (km)00 8.08.0
MotivationMotivationz
(km
)z
(km
)
00
2.02.0
Velocity model
x (km)x (km)00 8.08.0
km/s
4.54.5
1.51.5
Defocusing: lower resolution, distorted image
Multiples: image artifacts.
Problem:
KM: high frequency approximation.
Reason:
Solutions?
5
Solutions:Solutions:
MotivationMotivation
• Reverse time migration: solving two-way wave equationReverse time migration: solving two-way wave equation
Velocity modelKM image RTM image
• Target-oriented reverse time datuming:Target-oriented reverse time datuming: solving two-way wave equation to bypass overburdensolving two-way wave equation to bypass overburden
Luo, 2002: target-oriented RTDLuo, 2002: target-oriented RTDLuo and Schuster, 2004: bottom-up strategyLuo and Schuster, 2004: bottom-up strategy
Motivation Theory Numerical Tests Conclusions
6
MotivationMotivation
Motivation Theory Numerical Tests Conclusions
• RTD + Kirchhoff = accurate + cheap RTD + Kirchhoff = accurate + cheap
• RTD can reduce defocusing effectsRTD can reduce defocusing effects
RTDRTD
• Complex structures cause defocusing effectsComplex structures cause defocusing effects
• RTM is computationally expensiveRTM is computationally expensive
7
• Bottom-up strategy: computational efficiency Bottom-up strategy: computational efficiency
• Redatumed data can be used for least squares Redatumed data can be used for least squares migration and migration velocity analysis (MVA)migration and migration velocity analysis (MVA)
• Reduce defocusing effects for subsalt imagingReduce defocusing effects for subsalt imaging
• Closer to the target: better resolutionCloser to the target: better resolution
Motivation Theory Numerical Tests Conclusions
MotivationMotivation
8
OutlineOutline
• MotivationMotivation
• TheoryTheory
• ConclusionsConclusions
• Numerical TestsNumerical Tests 2-D SEG/EAGE salt model2-D SEG/EAGE salt model
3-D SEG/EAGE salt model3-D SEG/EAGE salt model
3-D field data3-D field data
Motivation Theory Numerical Tests Conclusions
9
d(s|r)d(s|r)
RRSS
x’x’ x’’x’’
Reverse time datumingReverse time datuming
TheoryTheory
Motivation Theory Numerical Tests Conclusions
10
SS
x’x’ x’’x’’
d(s|x”)=d(s|x”)= g*(r|x”)g*(r|x”) d(s|r)d(s|r)d(s|x’’)d(s|x’’)
Reverse time datumingReverse time datuming
RR
TheoryTheory
Motivation Theory Numerical Tests Conclusions
11
x’x’ x’’x’’
d(s|x”)=d(s|x”)= g*(r|x”)g*(r|x”) d(s|r)d(s|r)d(x’|x’’)d(x’|x’’)
d(x’|x”)=g*(s|x’) d(s|x”)d(x’|x”)=g*(s|x’) d(s|x”)
Reverse time datumingReverse time datuming
RRSS
TheoryTheory
Motivation Theory Numerical Tests Conclusions
12
TheoryTheory
Motivation Theory Numerical Tests Conclusions
Calculate Green’s functionsCalculate Green’s functions
Real source number Real source number on surface: 10on surface: 10
Virtual source number Virtual source number on datum: 3on datum: 3
VSP (source on surface) VSP (source on surface) Green’s functions: 10Green’s functions: 10
13
Calculate Green’s functionsCalculate Green’s functions
TheoryTheory
Motivation Theory Numerical Tests Conclusions
Real source number Real source number on surface: 10on surface: 10
Virtual source number Virtual source number on datum: 3on datum: 3
VSP (source on surface) VSP (source on surface) Green’s functions: 10Green’s functions: 10
RVSP (source on datum) RVSP (source on datum) Green’s functions: 3Green’s functions: 3
Reciprocity: RVSP=VSPReciprocity: RVSP=VSP
14
FD: Compute RVSP Green’s functions
Original data: FFT: time domain =>frequency domain
Crosscorrelation: Green’s functions with original data
WorkflowWorkflow
Motivation Theory Numerical Tests Conclusions
Reciprocity: RVSP =>VSP
Green’s functions: FFT: time domain => frequency domain
IFFT: frequency domain => time domain
Redatumed data
15
OutlineOutline
• MotivationMotivation
• TheoryTheory
• ConclusionsConclusions
• Numerical TestsNumerical Tests 2-D SEG/EAGE salt model2-D SEG/EAGE salt model
3-D SEG/EAGE salt model3-D SEG/EAGE salt model
3-D field data3-D field data
Motivation Theory Numerical Tests Conclusions
16Motivation Theory Numerical Tests Conclusions
z (k
m)
z (k
m)
00
2.02.0
Velocity model
x (km)x (km)00 8.08.0
km/s
4.54.5
1.51.5
Tim
e (s
)T
ime
(s)
00
4.04.0
RVSP Green’s function
x (km)x (km)00 8.08.0
2D SEG/EAGE Test2D SEG/EAGE Test
Tim
e (s
)T
ime
(s)
00
4.04.0
True CSG at datum
x (km)x (km)00 8.08.0
Tim
e (s
)T
ime
(s)
00
4.04.0
Redatumed CSG
x (km)x (km)00 8.08.0
17
z (k
m)
z (k
m)
00
2.02.0
Velocity model
x (km)x (km)00 8.08.0
km/s
4.54.5
1.51.5
z (k
m)
z (k
m)
00
2.02.0
KM image
x (km)x (km)00 8.08.0
z (k
m)
z (k
m)
00
2.02.0
RTM image
x (km)x (km)00 8.08.0
z (k
m)
z (k
m)
00
2.02.0
KM of redatumed data
x (km)x (km)00 8.08.0
Motivation Theory Numerical Tests Conclusions
2D SEG/EAGE Test2D SEG/EAGE Test
18
OutlineOutline
• MotivationMotivation
• TheoryTheory
• ConclusionsConclusions
• Numerical TestsNumerical Tests 2-D SEG/EAGE salt model2-D SEG/EAGE salt model
3-D SEG/EAGE salt model3-D SEG/EAGE salt model
3-D field data3-D field data
Motivation Theory Numerical Tests Conclusions
19
3D SEG/EAGE test3D SEG/EAGE test Z
(km
)Z
(km
)
00
2.02.0
y (km)y (km)
22
00
x (km)x (km)
3.53.5
00
km/s
4.54.5
1.51.5
Velocity model
SSP geometry:
1700 shots
1700 receivers
Datum depth:
1.5 km
RVSP Green’s functions:
850 shots
1700 receivers
Motivation Theory Numerical Tests Conclusions
20
3D SEG/EAGE test3D SEG/EAGE test
y (km)y (km)00 3.53.5
Tim
e (s
)T
ime
(s)
00
2.52.5
Original CSG
Motivation Theory Numerical Tests Conclusions
y (km)y (km)00 3.53.5
Tim
e (s
)T
ime
(s)
00
2.52.5
RVSP Green’s function
y (km)y (km)00 3.53.5
Tim
e (s
)T
ime
(s)
00
2.52.5
True CSG at datum
y (km)y (km)00 3.53.5
Tim
e (s
)T
ime
(s)
00
2.52.5
Redatumed CSG
21
Z (
km)
Z (
km)00
2.02.0
y (km)y (km)
22
00
x (km)x (km) 3.53.5
00
KM of original data
3D SEG/EAGE test3D SEG/EAGE test
Z (
km)
Z (
km)
00
2.02.0
y (km)y (km)
22
00
x (km)x (km) 3.53.5
00
KM of RTD data
Motivation Theory Numerical Tests Conclusions
22x (km)x (km)00 3.53.5
z (k
m)
z (k
m)
00
2.02.0
Velocity modelx (km)x (km)00 3.53.5
z (k
m)
z (k
m)
00
2.02.0
KM of original data
x (km)x (km)00 3.53.5
z (k
m)
z (k
m)
00
2.02.0
KM of redatumed data
( Inline No. 41 )( Inline No. 41 )
Motivation Theory Numerical Tests Conclusions
3D SEG/EAGE test3D SEG/EAGE test
23x (km)x (km)00 3.53.5
z (k
m)
z (k
m)
00
2.02.0
Velocity modelx (km)x (km)00 3.53.5
z (k
m)
z (k
m)
00
2.02.0
KM of original data
x (km)x (km)00 3.53.5
z (k
m)
z (k
m)
00
2.02.0
KM of redatumed data
( Inline No. 101 )( Inline No. 101 )
Motivation Theory Numerical Tests Conclusions
3D SEG/EAGE test3D SEG/EAGE test
24y (km)y (km)00 2.02.0
z (k
m)
z (k
m)
00
2.02.0
Velocity modely (km)y (km)00 2.02.0
z (k
m)
z (k
m)
00
2.02.0
KM of original data
y (km)y (km)00 2.02.0
z (k
m)
z (k
m)
00
2.02.0
KM of redatumed data
( Crossline No. 161 )( Crossline No. 161 )
Motivation Theory Numerical Tests Conclusions
3D SEG/EAGE test3D SEG/EAGE test
25y (km)y (km)00 2.02.0
z (k
m)
z (k
m)
00
2.02.0
Velocity modely (km)y (km)00 2.02.0
z (k
m)
z (k
m)
00
2.02.0
KM of original data
y (km)y (km)00 2.02.0
z (k
m)
z (k
m)
00
2.02.0
KM of redatumed data
( Crossline No. 201 )( Crossline No. 201 )
Motivation Theory Numerical Tests Conclusions
3D SEG/EAGE test3D SEG/EAGE test
26x (km)x (km)00 3.53.5
y (k
m)
y (k
m)
00
2.02.0
Velocity modelx (km)x (km)00 3.53.5
y (k
m)
y (k
m)
00
2.02.0
KM of original data
x (km)x (km)00 3.53.5
y (k
m)
y (k
m)
00
2.02.0
KM of redatumed data
( depth: z=1.4 km )( depth: z=1.4 km )
Motivation Theory Numerical Tests Conclusions
3D SEG/EAGE test3D SEG/EAGE test
27x (km)x (km)00 3.53.5
y (k
m)
y (k
m)
00
2.02.0
Velocity modelx (km)x (km)00 3.53.5
y (k
m)
y (k
m)
00
2.02.0
KM of original data
x (km)x (km)00 3.53.5
y (k
m)
y (k
m)
00
2.02.0
KM of redatumed data
( depth: z=1.5 km )( depth: z=1.5 km )
Motivation Theory Numerical Tests Conclusions
3D SEG/EAGE test3D SEG/EAGE test
28
OutlineOutline
• MotivationMotivation
• TheoryTheory
• ConclusionsConclusions
• Numerical TestsNumerical Tests 2-D SEG/EAGE salt model2-D SEG/EAGE salt model
3-D SEG/EAGE salt model3-D SEG/EAGE salt model
3-D field data3-D field data
Motivation Theory Numerical Tests Conclusions
29
Z (
km)
Z (
km)
00
8.08.0
y (km)y (km)
6.06.0
00
x (km)x (km)
1212
00
Interval velocity model
3D Field Data Test3D Field Data Test
OBC geometry:
50,000 shots
180 receivers per shot
Datum depth:
1.5 km
RVSP Green’s functions:
5,000 shots
180 receivers per shot
km/s5.55.5
1.51.5
Motivation Theory Numerical Tests Conclusions
30
3D Field Data Test3D Field Data Test
y (km)y (km)00 4.54.5
Tim
e (s
)T
ime
(s)
00
6.06.0
Original CSG
y (km)y (km)00 4.54.5
Tim
e (s
)T
ime
(s)
00
6.06.0
Redatumed CSG
Motivation Theory Numerical Tests Conclusions
31
KM of RTD data
Z (
km)
Z (
km)00
88
y (km)y (km)
55
00
x (km)x (km) 1212
00
KM of original data
Z (
km)
Z (
km)
00
88
y (km)y (km)
55
00
x (km)x (km) 1212
00
KM of redatumed data
Motivation Theory Numerical Tests Conclusions
3D Field Data Test3D Field Data Test
32
X (km)X (km)00 1212
Z (
km)
Z (
km)
00
8.08.0
KM of original data KM of RTD data
( Inline No. 21 )( Inline No. 21 )
X (km)X (km)00 1212
Z (
km)
Z (
km)
00
8.08.0
Motivation Theory Numerical Tests Conclusions
3D Field Data Test3D Field Data Test
33
( Inline No. 41 )( Inline No. 41 )
X (km)X (km)00 1212
Z (
km)
Z (
km)
00
8.08.0
KM of RTD data
X (km)X (km)00 1212
Z (
km)
Z (
km)
00
8.08.0
Motivation Theory Numerical Tests Conclusions
3D Field Data Test3D Field Data Test
KM of original data
34
( Inline No. 61 )( Inline No. 61 )
X (km)X (km)00 1212
Z (
km)
Z (
km)
00
8.08.0
KM of RTD data
X (km)X (km)00 1212
Z (
km)
Z (
km)
00
8.08.0
Motivation Theory Numerical Tests Conclusions
3D Field Data Test3D Field Data Test
KM of original data
35
( Crossline No. 41 )( Crossline No. 41 )
Y (km)Y (km)00 5.05.0
Z (
km)
Z (
km)
00
8.08.0
KM of RTD data
Y (km)Y (km)00 5.05.0
Z (
km)
Z (
km)
00
8.08.0
Motivation Theory Numerical Tests Conclusions
3D Field Data Test3D Field Data Test
KM of original data
36
( Crossline No. 61 )( Crossline No. 61 )
Y (km)Y (km)00 5.05.0
Z (
km)
Z (
km)
00
8.08.0
KM of RTD data
Y (km)Y (km)00 5.05.0
Z (
km)
Z (
km)
00
8.08.0
Motivation Theory Numerical Tests Conclusions
3D Field Data Test3D Field Data Test
KM of original data
37
( Crossline No. 81 )( Crossline No. 81 )
Y (km)Y (km)00 5.05.0
Z (
km)
Z (
km)
00
8.08.0
KM of RTD data
Y (km)Y (km)00 5.05.0
Z (
km)
Z (
km)
00
8.08.0
Motivation Theory Numerical Tests Conclusions
3D Field Data Test3D Field Data Test
KM of original data
38
( Depth 2.0 km )( Depth 2.0 km )
X (km)X (km)00 1212
Y (
km)
Y (
km)
00
5.05.0
KM of RTD data
X (km)X (km)00 1212
Y (
km)
Y (
km)
00
5.05.0
Motivation Theory Numerical Tests Conclusions
3D Field Data Test3D Field Data Test
KM of original data
39
( Depth 2.5 km )( Depth 2.5 km )
X (km)X (km)00 1212
Y (
km)
Y (
km)
00
5.05.0
KM of RTD data
X (km)X (km)00 1212
Y (
km)
Y (
km)
00
5.05.0
Motivation Theory Numerical Tests Conclusions
3D Field Data Test3D Field Data Test
KM of original data
40
( Depth 4.0 km )( Depth 4.0 km )
X (km)X (km)00 1212
Y (
km)
Y (
km)
00
5.05.0
KM of RTD data
X (km)X (km)00 1212
Y (
km)
Y (
km)
00
5.05.0
Motivation Theory Numerical Tests Conclusions
3D Field Data Test3D Field Data Test
KM of original data
41
RTM
(CPU-hours)
RTD
(CPU-hours)
Speed up
2D SEG/EAGE test
21.0 6.5 3
3D SEG/EAGE test
16,000
(estimated)1,866 9
3D filed data test5,000,000
(estimated)52,000 100
Computational CostsComputational Costs
Motivation Theory Numerical Tests Conclusions
42
OutlineOutline
• MotivationMotivation
• TheoryTheory
• ConclusionsConclusions
• Numerical TestsNumerical Tests 2-D SEG/EAGE salt model2-D SEG/EAGE salt model
3-D SEG/EAGE salt model3-D SEG/EAGE salt model
3-D field data3-D field data
Motivation Theory Numerical Tests Conclusions
43
KM of RTD achieved image quality comparable to RTM KM of RTD achieved image quality comparable to RTM
at much lower cost.at much lower cost.
Motivation Theory Numerical Tests Conclusions
• 2-D numerical test2-D numerical test
3-D RTD is implemented for synthetic and GOM data at 3-D RTD is implemented for synthetic and GOM data at acceptable computational cost;acceptable computational cost;
• 3-D numerical test3-D numerical test
Apparent improvements in mage quality are achieved Apparent improvements in mage quality are achieved compared to KM image of original data.compared to KM image of original data.
• Future applicationFuture application
Subsalt least suqares migration and migration velocity Subsalt least suqares migration and migration velocity analysisanalysis
ConclusionsConclusions
44
AcknowledgementsAcknowledgements• Dr. Gerard Schuster and my committee members:
Dr. Michael Zhdanov, Dr. Richard D. Jarrard for their advice and constructive criticism;
• UTAM friends:– Dr. Xiang Xiao, Weiping Cao, and Chaiwoot Boonyasiriwat
for their help on my thesis research;
– Ge Zhang for his experiences on field data processing;
– Dr. Sherif Hanafy, Shengdong Liu, Naoshi Aoki and all other UTAM members for their support in my life and work;
• CHPC for the computation support.
45
Thanks!Thanks!
46Motivation Theory Numerical Tests Conclusions
z (k
m)
z (k
m)
00
2.02.0
Velocity model
x (km)x (km)00 8.08.0
km/s
4.54.5
1.51.5
z (k
m)
z (k
m)
00
2.02.0
KM image
x (km)x (km)00 8.08.0
Tim
e (s
)T
ime
(s)
00
4.04.0
Common shot gather
x (km)x (km)00 8.08.0
MotivationMotivation
z (k
m)
z (k
m)
00
2.02.0
RTM image
x (km)x (km)00 8.08.0
47
d(s|r)d(s|r)
RRSS
x’x’ x’’x’’
Traditional reverse time datumingTraditional reverse time datuming
TheoryTheory
Motivation Theory Numerical Tests Conclusions
48
SS
x’x’ x’’x’’
d(s|x”)=d(s|x”)= g*(r|x”)g*(r|x”) d(s|r)d(s|r)d(s|x’’)d(s|x’’)
Reverse time DatumingReverse time Datuming
RR
TheoryTheory
Motivation Theory Numerical Tests Conclusions
49
x’x’ x’’x’’
d(s|x”)=d(s|x”)= g*(r|x”)g*(r|x”) d(s|r)d(s|r)d(x’|x’’)d(x’|x’’)
d(x’|x”)=g*(s|x’) d(s|x”)d(x’|x”)=g*(s|x’) d(s|x”)
Reverse time DatumingReverse time Datuming
RRSS
TheoryTheory
Motivation Theory Numerical Tests Conclusions
50
Target-oriented RTDTarget-oriented RTD(Luo , 2006)(Luo , 2006)
TheoryTheory
Motivation Theory Numerical Tests Conclusions
51
Target-oriented RTDTarget-oriented RTD(Luo , 2006)(Luo , 2006)
g(s|x’)g(s|x’) g(r|x”)g(r|x”)** d(s|r)d(s|r)
= d(x’|x’’)= d(x’|x’’)
TheoryTheory
Motivation Theory Numerical Tests Conclusions
52
Target-oriented RTDTarget-oriented RTD(Luo , 2006)(Luo , 2006)
TheoryTheory
Motivation Theory Numerical Tests Conclusions
g(s|x’)g(s|x’) g(r|x")g(r|x")** d(s|r)d(s|r)
= d(x’|x’’)= d(x’|x’’)
53
Compute VSP Green’s functions in time domain
Original data: time domain to frequency domain
Green’s functions: Time domain to frequency domain
Reverse time datum for different frequency
WorkflowWorkflow
Sum over frequency
Redatumed data: frequency domain to time domain
Motivation Theory Numerical Tests Conclusions
54
FD: Compute RVSP Green’s functions
Original data: FFT: time domain =>frequency domain
Crosscorrelation: Green’s functions with original data
WorkflowWorkflow
Motivation Theory Numerical Tests Conclusions
Reciprocity: RVSP =>VSP
Green’s functions: FFT: time domain => frequency domain
Sum over frequency
IFFT: frequency domain => time domain
Redatumed data
55
ConclusionsConclusions
• Bottom-up strategy: computational efficiency Bottom-up strategy: computational efficiency
• Redatumed data can be used by LSM & MVARedatumed data can be used by LSM & MVA
• Reduce defocusing effects for subsalt imagingReduce defocusing effects for subsalt imaging
• Closer to the target: better resolutionCloser to the target: better resolution
Benefits:Benefits:
Limitations:Limitations:
•Extra I/O for accessing Green’s functionsExtra I/O for accessing Green’s functions
Motivation Theory Numerical Tests Conclusions
Top Related