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Robust Imaging for RVSP Data with Static Errors. Jianhua Yu University of Utah. Problems in RVSP imaging. How to eliminate the static errors in RVSP imaging. Synthetic and real data examples. Summary. Outline. Problems in RVSP imaging. Outline. - PowerPoint PPT Presentation
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Jianhua Yu
University of Utah
Robust Imaging for RVSP Data with Static Errors
OutlineOutlineProblems in RVSP imaging
How to eliminate the static errors in RVSP imaging
Synthetic and real data examples
Summary
OutlineOutlineProblems in RVSP imaging
How to eliminate the static errors in RVSP imaging
Synthetic and real data examples
Summary
Well Geophone
?
RVSP Acquistion GeometryRVSP Acquistion Geometry
Problems in RVSP imaging
Don’t know initial time
Have limitation to deviated well
Don’t know source wavelet
Static shift error may exist
• Do not require source waveletDo not require source wavelet• Image conditions:Image conditions:
(a) Primary reflection (solid line)
sgxgsxprimary
(b) ghost reflection (solid line)
sgxgxgsgghost
''
Autocorrelogram MigrationAutocorrelogram Migration
Migration Formula:Migration Formula:
sg
sgxgsxsgp)()( xm
Migrated ImageMigrated Image Autocorrelograms of seismic data
sg
sgxgxxsxsgg)()(
00xm
OutlineOutlineProblems in RVSP imaging
How to eliminate the static errors in RVSP imaging
Synthetic and real data examples
Summary
sg
xgsxp
ssxsx '
Primary Auto. Migration:
gxgxg '
gssgsg '
sgxgsxp ''''
sgxgsx
OutlineOutlineProblems in RVSP imaging
How to eliminate the static errors in RVSP imaging
Synthetic and real data examples
Summary
Dep
th (ft)
1750
250
RVSP and Geology Model98
sou
rces
25 geophones
625 ftWell
Tim
e (s
)
2.5
0110.03 0.03
CRG 16
Depth (kft) Depth (kft)
CRG 2
Tim
e (s
)
2.5
0110.03 0.03
CRG 2
Depth (kft) Depth (kft)
CRG 16
Tim
e (s
)
2.0
0110.03 0.03
Autocorr. CRG 2
Depth (kft) Depth (kft)
Autocorr. CRG 16
Dep
th (
ft)
1750
0No static errors With static errors
Primary Kichhoff Migration
Dep
th (
ft)
1750
0No static errors With static errors
Primary Autocorr. Migration
Dep
th (
ft)
1750
0
Kirchhoff Autocorr. Mig
Dep
th (
ft)
1500
250No static errors With static errors
Ghost Kirchhoff Migration
Dep
th (
ft)
1500
250No static errors With static errors
Ghost Autocorr. Migration
Dep
th (
ft)
1500
250
Kirchhoff Autocorr Mig (Ghost)
OutlineOutlineProblems in RVSP imaging
How to eliminate the static errors in RVSP imaging
Synthetic and real data examples
Summary
Tim
e (s
)
0.35
00.030.031 1
CRG 6 CRG 15
Depth (kft) Depth (kft)
Tim
e (s
)
0.30
00.030.031 1
CRG 6: primary CRG 15: primary
Depth (kft) Depth (kft)
Tim
e (s
)
0.30
00.030.031 1
Autocorr. CRG 6: primary Autocorr. CRG 15: primary
Depth (kft) Depth (kft)
Tim
e (s
)
0.30
00.030.031 1
Autocorr. CRG 6: ghost Autocorr. CRG 15: ghost
Depth (kft) Depth (kft)
Dep
th (
ft)
1200
150
Kirchhoff Mig. with Primary120 ft
Dep
th (
ft)
1000
0
Well Log and Kirchhoff Migration
Dep
th (
ft)
1200
150
Autocorr. Mig. with Primary120 ft
Dep
th (
ft)
1000
0
Well Log and Primary Autocorr. Mig
Dep
th (
ft)
Kirchhoff Mig.Kirchhoff Mig. Primary Auto. Mig.Primary Auto. Mig.
WellWell Well Well MigMig Mig.Mig.
010
00
Primary autocorrelogram migration gives better correlation with well log synthetic seismograms
Dep
th (
ft)
1200
150
Auto mig. with P+G120 ft
Dep
th (
ft)
1000
0
Well Log and Mig without Separating Primary and Ghost
Dep
th (
ft)
1200
150
Joint mig. with P+G120 ft
Dep
th (
ft)
1000
0
Well log and Joint Mig. with Primary and Ghost
Dep
th (
ft)
Mig. With P+GMig. With P+G
WellWell MigMig
010
00Joint Mig. With P+GJoint Mig. With P+G
WellWell MigMig
OutlineOutlineProblems in RVSP imaging
How to eliminate the static errors in RVSP imaging
Synthetic and real data examples
Summary
SummarySummary
• Autocorrelogram imaging condition is Autocorrelogram imaging condition is capable of suppressing static shift capable of suppressing static shift errors errors
• It can be used to image reflectivity with It can be used to image reflectivity with static error-polluted VSP and drill-bit static error-polluted VSP and drill-bit data where no pilot signal is availabledata where no pilot signal is available
AcknowledgmentsAcknowledgments
• Thank 2002 sponsors of UTAM Thank 2002 sponsors of UTAM consortium for the financial supportconsortium for the financial support
• Thank Thank ExxonExxon for providing the real data for providing the real data