Jianhua Yu University of Utah

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