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Improve Migration Image Quality Improve Migration Image Quality by 3-D Migration Deconvolutionby 3-D Migration Deconvolution
Jianhua Yu, Gerard T. Schuster Jianhua Yu, Gerard T. Schuster
University of Utah University of Utah
Motivation
OutlineOutline
Migration Deconvolution
Examples
Conclusions
Implementation of MD
Migration noise and artifacts
Seismic Migration NoiseSeismic Migration Noise0.5
3.5
Dep
th (
km
)
Footprint
Weak illumination
Limit recording aperture
What Affects Seismic Migration Quality What Affects Seismic Migration Quality
Irregular acquisition geometry
Incorrect velocity
High order phenomenon: anisotropy, attenuation etc.
Bandlimited wavelet
Noise and artifacts
Migration Image suffers fromMigration Image suffers from
Poor spatial resolution
Non-uniform illumination
Objective :
Develop 3-D migration deconvolution
Limit recording aperture
Irregular acquisition geometry
to deblur the influence of
Objective :
Improving spatial resolution
Enhancing illumination
Suppressing migration noise and artifacts
Motivation
OutlineOutline
Migration Deconvolution (MD)
Examples
Conclusions
Implementation of MD
mm = = G dG dTT butbut dd = G = G RRMigrated Section Data
G RG R
mm = = PSF(R)PSF(R) Migration image = Blurred image of Migration image = Blurred image of
true reflectivity modeltrue reflectivity model
Migration operator
Migration Deconvolution TheoryMigration Deconvolution Theory
mRG GT
Migration imageReflectivity
Migration Green’s function
Migration Deconvolution TheoryMigration Deconvolution Theory
mRG GT
G GT -1
][ G GT -1
][
11
Migration Deconvolution TheoryMigration Deconvolution Theory
mR
G GT -1
][
1
Migration Deconvolution TheoryMigration Deconvolution Theory
sgsoogsgomig rdrdrrGrrGrrGrrGrr
)()()()()( **Migration Green’s functionMigration Green’s function
(Schuster et al., 2000)(Schuster et al., 2000)
ooomig rdrRrrrm
)()()(
sgsoogsgomig rdrdrrGrrGrrGrrGrr
)()()()()( **
Migration Deconvolution TheoryMigration Deconvolution Theory
Lateral shift invariant migration Green’s functionLateral shift invariant migration Green’s function
Reduction of MD cost
),( pp yx --- --- Reference position of migration Green’s functionReference position of migration Green’s function
),,,,( oppoomig zyxzyyxx
oooooo dzdydxzyxR ),,(
)(rm
In wavenumber-space domain:
Rm
Motivation
OutlineOutline
Migration Deconvolution (MD)
Examples
Conclusions
Implementation of MD
MD Implementation Steps:MD Implementation Steps:
Step 1: Prepare traveltime table
Velocity cube
Acquisition geometry information
Step 2: Calculate the migration Green’s function at the depth Zi ),,,,( ippj zyxzyx
Step 3: Obtain MD image at the depth Zi by solving following equation
Rm
MD Implementation Steps:MD Implementation Steps:
Step 4: Repeat Steps 2-3 until the maximum depth is finished
Motivation
OutlineOutline
Migration Deconvolution (MD)
Examples: Synthetic data
Conclusions
Implementation of MD
Recording Geometry
: Sources : Sources : Receivers: Receivers
0
3 X (km)03
Y (km)0
3 X (km)03
Y (km)
0
3 X (km)03
Y (km)
0
3 X (km)03
Y (km)
0
3 X (km)03
Y (km)
0
3 X (km)03
Y (km)
MIG MD
Z=1 km
Z=3 km
Z=5 km
Depth Slices
0
3 X (km)03
Y (km)0
3 X (km)03
Y (km)
0
3 X (km)03
Y (km)
0
3 X (km)03
Y (km)
0
3 X (km)03
Y (km)
0
3 X (km)03
Y (km)
MIG MD
Z=7 km
Z=9 km
Z=10 km
Depth Slices
00
2.5 km2.5 km
00
Meandering Stream Model
2.5 km2.5 km
5 x 1 Sources; 11 x 7 Receivers5 x 1 Sources; 11 x 7 Receivers
3.5
km
MigMig
MDMD
ModelModel
0 Y (km)
X (km
)
2.5
0
2.5
Z=3.5 KM
VSP Geometry: source 21 x 21; geophone: 12
Depth=1.75 kmMigration MD
GOM Velocity Model X (km)
Dep
th (
km
)
12
0
2 10
X (km)
Dep
th (
km
)
10
8
4 10 X (km)4 10
Migration Migration+MD
X (km)
Dep
th (
km
)
10
8.5
4 10 X (km)4 10
Migration Migration+MD
00
12.2 km12.2 km
00
3-D SEG/EAGE Salt Model
12.2 km12.2 km
9 x 5 Sources; 9 x 5 Sources; dxshot=dyshot=1 km
201 x 201 Receivers201 x 201 Receivers
Imaging: dx=dy=20 m
3-D SEG/EAGE Salt Model
X (km)Y (km)
Y=7.12 km
Mig z = 1.4 km MDX (km)
3
10
Y (
km
)
5 9.8 5 9.8X (km)
Mig (z=1.2 km)X (km)
3
10
Y (
km
)
5 9.8 5 9.8X (km)
MD (z=1.2 km)
X (km)0 203
10
Dep
th (
km
)Sigsbee2B Model
X (km)0 202.5
10
Dep
th (
km
)
Mig
X (km)0 202.5
10
Dep
th (
km
)
MD
5
10
Dep
th (
km
)Mig.
MD
Motivation
OutlineOutline
Migration Deconvolution (MD)
Examples: 2-D field data
Conclusions
Implementation of MD
PS PSTM Image ( by Unocal)PS PSTM Image ( by Unocal)
0 6X (km)
0
8
Tim
e (s
)
0 6X (km)
0
8
Tim
e (s
)
MDMDPSTM(courtesy of Unocal)PSTM(courtesy of Unocal) PSTMDPSTMD
0 6X (km)
3
8
Tim
e (s
)
MDMDPSTM(courtesy of Unocal) PSTMD
MD
Tim
e (s
)Mig (courtesy of Aramco)
Tim
e (s
)Mig (Courtesy of Aramco) MD
Mig (Courtesy of Aramco) MD
Motivation
OutlineOutline
Migration Deconvolution (MD)
Examples: 3-D field data
Conclusions
Implementation of MD
1.6 s1.6 s
Inline
Cro
sslin
e3D PSTM (courtesy of Unocal) MD
2.0 s2.0 s
Cro
ssli
ne
3D PSTM (courtesy of Unocal) MD
3.0
Mig in Inline (Courtesy of Unocal) MDT
imes
(s)
1.2
Mig (Courtesy of Unocal) MDInline Number1 90 1 90
1
300
Cro
sslin
e N
um
ber
Inline Number
(2 kft)
(3.6 kft)
Inline Number1 90 1 901
265
Cro
sslin
e N
um
ber
Inline Number
Mig MD
Inline Number1 901.1
7.0
Dep
th (
kft
)
90 Inline Number1
(Crossline=50)
Mig (courtesy of Unocal) MD
(crossline 200)
1 90 1 901.1
8.0
Dep
th (
kft
)Mig (courtesy of Unocal) MD
Inline Number Inline Number
Motivation
OutlineOutline
Migration Deconvolution (MD)
Examples
Conclusions
Implementation of MD
ConclusionsConclusions
Suppress migration noise
Improve spatial resolution
MD cost is related to acquisition geometry
V(z) assumption for moderately complex models
AcknowledgementsAcknowledgements
UTAM Sponsors
SMAART Joint Venture
Aramco,Aramco, BP and Unocal