3-D Migration Deconvolution

3-D Migration Deconvolution3-D Migration Deconvolution

Jianxing Hu, GXTJianxing Hu, GXTBob Estill, Unocal Bob Estill, Unocal

Jianhua Yu, University of UtahJianhua Yu, University of UtahGerard T. Schuster, University of Utah Gerard T. Schuster, University of Utah

Why Do Migration Deconvolution (MD) ?

OutlineOutline

Migration Deconvolution

Examples

Conclusions

Implementation of MD


OutlineOutline


Examples

Conclusions


Migration noise and artifacts

Migration Noise ProblemsMigration Noise Problems0

3.5

Dep

th (

km)

Weak illumination

Footprint

Purpose of MD Processing:

Improving spatial resolution

Enhancing illumination

Suppressing migration noise and artifacts


OutlineOutline


Examples

Conclusions


M = M = L LTT

Migration:

Migrated imageMigrated image

L RL R

L is modeling operatorL is modeling operator

ReflectivityReflectivity

TTRR = ( = (L LL L ) M ) M

-1-1

3-D PRESTACK MD3-D PRESTACK MD

ReflectivityReflectivity

Design an improved MD filter

Migrated SectionMigrated Section

MD is to eliminate the blurring influence in migration image by designing MD operator

Goal:


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Examples

Conclusions


MD Implementation Steps:MD Implementation Steps:

Step 1: Prepare traveltime table

Velocity cube

Acquisition geometry information

or

Use migration timetable

Calculate the migration Green’s function


Step 2:Y (km)

Dep

th (

km

)

Depth LevelDepth Level ii N

L

Step 4: Invert MD image at the depth Zi by solving linear equations

R


Step 5: Repeat Steps 2-4 until the maximum depth is finished


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Examples : Synthetic data

Conclusions


00

3 km3 km

00

3-D Point Scatterer Model

3 km3 km

11 X 11 Receivers11 X 11 Receivers dxg=dyg=0.3 km

Imaging: dx=dy=50 m

dz=100 m

3X3 Sources; 3X3 Sources; dxshot=dyshot=1.5 km

10 k

m

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

00

12.2 km12.2 km

00

3-D SEG/EAGE Salt Model

12.2 km12.2 km

9 X5 Sources; 9 X5 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 and MD ( z=1.4 km, negative polarity)

X (km)3

10

Y (

km

)

5 9.8 5 9.8X (km)

MDMig

MD (z=1.2 km)Mig (z=1.2 km)X (km)

3

10

Y (

km

)

5 9.8 5 9.8X (km)

MD (z=1.2 km)Mig (z=1.2 km)


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Examples: 2-D field data

Conclusions


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)PSTM(courtesy of Unocal) PSTMDPSTMD


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Examples: 3-D field data

Conclusions


3-D Land Field Data

: Receivers: Receivers: Sources : Sources

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

Mig in Inline (Courtesy of Unocal) MD

Mig

MD

Mig

MD

Mig (Courtesy of Unocal) MDInline Number1 90 1 90

1

300

Cro

sslin

e N

um

ber

Inline Number

(2 kft)

Fault

Fault

(3.6 kft)

Inline Number1 90 1 901

265

Cro

sslin

e N

um

ber

Inline Number

Mig (Courtesy of Unocal) MD

Inline Number1 901.1

7.0

Dep

th (

kft

)

90 Inline Number1

Mig (courtesy of Unocal) MD

(Crossline=50)

(crossline 200)

1 90 1 901.1

8.0

Dep

th (

kft

)Mig (courtesy of Unocal) MD

1 2501.1

7.0

Dep

th (

kft

)Crossline Number

7.01.1

(Inline =50)

Mig (Unocal)

MD


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Examples

Conclusions


ConclusionsConclusions

Aperture width and filter length in designing MD filter are two key parameters

Improve resolution and suppress migration artifacts

MD cost is related with acquisition geometry

AcknowledgmentsAcknowledgments• Thank Thank Amramco, UnocalAmramco, Unocal, and , and ChevronChevron--

Texaco Texaco for providing the data sets for providing the data sets

• Thank 2002 Thank 2002 UTAM sponsorsUTAM sponsors for their for their financial supportfinancial support

• The help and comments from The help and comments from Alan LeedsAlan Leeds and and George YaoGeorge Yao are very appreciated are very appreciated

• http://utam.gg.utah.eduhttp://utam.gg.utah.edu

Documents

3-D Migration Deconvolution