Impact of MD on AVO InversionImpact of MD on AVO Inversion

Jianhua YuUniversity of Utah

OutlineOutlineMotivationMethodologyNumerical Tests

Conclusions

Synthetic dataField marine data

Prestack migration based Prestack migration based AVO InversionAVO Inversion

Prestack migration to generate Prestack migration to generate the common offset data, CRGs, the common offset data, CRGs, and angle gathers and angle gathers

AVO analysis or inversion AVO analysis or inversion (Shuey, 1985)(Shuey, 1985)

What Influences the What Influences the Accuracy of AVO?Accuracy of AVO?

Preprocessing such as amplitude Preprocessing such as amplitude balance, demultiple etc. balance, demultiple etc.

Migration noise, footprint due to Migration noise, footprint due to coarse acquisition coarse acquisition

Migration ProblemMigration ProblemSeismic Trace

GS

Layer 2

Layer 1

Incorrect Contribution

Actual reflection point

Migration Ellipse

Migration DeconvolutionMigration Deconvolution

• Improve prestack migration Improve prestack migration imageimage

• Reduce prestack migration Reduce prestack migration noise and artifactsnoise and artifacts

MotivationMotivation

• Improve data for AVO Improve data for AVO analysis and seismic attribute analysis and seismic attribute analysisanalysis

• Develop a MD-AVO methodDevelop a MD-AVO method

• Reduce migration artifactsReduce migration artifacts

OutlineOutlineMotivationMethodologyNumerical Tests

Conclusions

Synthetic dataField marine data

m’ = m’ = L L ddTT

Migration Deconvolution

butbut dd = L = L mmMigrated SectionMigrated Section Data

L mL m

Migration SectionMigration Section == Blurred Blurred Image ofImage of true reflectivity model true reflectivity model mm

TTmm = ( = (L LL L ) m’ ) m’

-1-1

How to Get the True Reflectivity How to Get the True Reflectivity Model Model mm

Deconvolve the point scatterer response from the migration image

ReflectivityReflectivity

Deblurring filter

Migrated SectionMigrated Section

How MD conjunct with AVOHow MD conjunct with AVO

Data in common offset domain Data in common offset domain satisfies the local property of MD satisfies the local property of MD filter filter

Common offset section is natural Common offset section is natural domain for AVO analysis domain for AVO analysis

Processing Steps:Processing Steps:Preprocessing : Geometric spreading Preprocessing : Geometric spreading correction, amplitude balancing, and correction, amplitude balancing, and demultipledemultiple

Prestack migration/inversion to generate the Prestack migration/inversion to generate the migrated COG and angle gathersmigrated COG and angle gathers

Velocity analysis and estimate RMS velocity Velocity analysis and estimate RMS velocity model for migration in time domainmodel for migration in time domain

MD-AVO MethodologyMD-AVO Methodology

Apply MD to common offset sectionsApply MD to common offset sections

Normal AVO parameter inversionNormal AVO parameter inversion

Apply MD to AVO sectionApply MD to AVO section

OutlineOutlineMotivationMethodologyNumerical Tests

Conclusions

Synthetic dataField marine data

OutlineOutlineMotivationMethodologyNumerical Tests

Conclusions

Synthetic dataField marine data

Ti m

e ( s

)

0

2.5

1 5 1 5 0

2.5

CDP 150

X(km) X(km)

Prestack Migrated COG (45-55) SectionPrestack Migrated COG (45-55) Section

Mig + MDMig

Tim

e (s

)

0.5

2.5

1 2 1 20.5

2.5

CDP 150

X(km) X(km)Closeup of COG (45-55) SectionCloseup of COG (45-55) Section

Mig+ MDMig

Fr e

qu

e nc y

(H

z)

0.0

60

100 110 100 1100.0

60

CDP 150

Trace No. Trace No.

Spectrums of Mig and MD ImagesSpectrums of Mig and MD Images

Mig + MDMig

Tim

e (s

)

0.6

1.8

1 1.8 1 1.80.6

1.8

X(km) X(km)Close-up of One CRG Close-up of One CRG

Mig + MDMig

OutlineOutlineMotivationMethodologyNumerical Tests

Conclusions

Synthetic dataField marine data

Ti m

e ( s

)1.0

3.0

0.26 2.0 1.5 3.51.0

3.0

CDP 150

Offset (km) Velocity (km/s)

CDP 150

Off

s et

(km

)

-6.0

-3.5

200 800Shot Number

-6.0

-3.5

RMS Amp. before and after preprocessing

Raw data

After preprocessed

1.442

0.322

Least Squares Inversion for Demultiples (Taner et al. 1969; Lumely et al., 1998; Zhao, 1996)

Velocity model

Hyperbolic operator

Seismic data

Transpose of L

Get ghosts: Dg=Lmg

Primary: dp=d-dg

m = (m = (L L L L )) L L ddTTTT -1

Ti m

e ( s

)

0.0

3.0

0.26 2.0

Offset (km)

CDP1300 CDP1300 CDP1300

0.26 2.0

Offset (km)

0.26 2.0

Offset (km)

Raw data Demultiple Multiples

Ti m

e ( s

)0.0

3.0

0.26 2.0 0.26 2.00.0

3.0

CDP 1300 CDP 1300

Offset (km) Offset (km)

NMO raw data NMO demultiple

Ti m

e ( s

)0.0

3.0

1.5 3.50.0

3.0

CDP 1300 CDP 1300

Velocity (km/s) Velocity (km/s)1.5 3.5

Raw data Demultiple

Ti m

e ( s

)0.0

3.0

0.26 2.0 0.26 2.00.0

3.0

CDP 1764 CDP 1764

Offset (km) Offset (km)

AVO AVO ? ?

2.1

NMO raw data NMO demultiple

Ti m

e ( s

)

0

5.0

0 21X (km)

RMS Velocity Model

1 .5

3 .5

m/s

Ve l

oci t

y ( k

m/ s

)

5

1

0 3Time (s)

Comparison of Estimated RMS Velocity and Well Sonic Data

Well VintWell Vrms

Estimated Vrms

Ti m

e ( s

)

0

3.5

7 20X (km)

Stacked Section WELL

Ti m

e ( s

)

0

3.5

7 20X (km)

Migration Section

Ti m

e ( s

)

0

3.5

7 20X (km)

MD Result

Ti m

e ( s

)

0

3.5

12 18X (km)

Comparison of Mig and MD 18 12 X (km)

Mig+MDMig

Reservoir Reservoir

Tim

e (s

)

1.98

2.20

12.1 13.6

1.98

2.20

X (km)

Before MD

After MD

AVO Parameter : PP

SS*

Reservoir

Reservoir

- -3.6

- +2.3

Ti m

e ( s

)

1.6

2.7

7 18 7 18X (km) X (km)

HCI Section Before and After MDHCI Section Before and After MD

Before MD After MD

Reservoir Reservoir

OutlineOutlineMotivationMethodologyNumerical Tests

Conclusions

Synthetic dataField marine data

ConclusionsConclusions

Improves stratigraphic resolutionImproves stratigraphic resolution

Helps to identify lithology Helps to identify lithology anomaly in AVO section anomaly in AVO section

Attenuates migration noise andAttenuates migration noise and artifactsartifacts

Future WorkFuture Work

Develop 3-D Prestack MD for Develop 3-D Prestack MD for Field Data ProcessingField Data Processing

Blind Test on More Real DataBlind Test on More Real Data(We look forward to the donation (We look forward to the donation of data from sponsors)of data from sponsors)

AcknowledgmentAcknowledgment

• Thank 2001 UTAM sponsors for Thank 2001 UTAM sponsors for the financial supportthe financial support