3-D Migration Deconvolution: 3-D Migration Deconvolution: Real ExamplesReal Examples

Jianhua Yu University of UtahJianhua Yu University of UtahBob Estill Unocal Bob Estill Unocal

Motivation

OutlineOutline

3-D Migration Deconvolution (MD)

Examples

Conclusions

Motivation

OutlineOutline

3-D Migration Deconvolution (MD)

Examples

Conclusions

3-D prestack time migration data

Unocal 3-D data from Alaska Unocal 3-D data from Alaska

2002 Winter:

3.0

Mig in Inline (Courtesy of Unocal) MDT

imes

(s)

1.2

3-D prestack time migration data

3-D prestack depth migration data

2003 Summer: 2003 Summer:

Unocal 3-D data from Alaska Unocal 3-D data from Alaska

2002 Winter:

Does MD work equally well for both time and depth migration?

3-D prestack depth migration data

2003 Summer: 2003 Summer:

Objectives : Objectives :

Improve spatial resolution

Enhance illumination

Suppress migration noise and artifacts

Apply 3-D migration deconvolution in depth domain

Motivation

OutlineOutline

3-D Migration Deconvolution (MD)

Examples

Conclusions

TT RR = ( = (L LL L ) m ) m

-1-1

Migration Deconvolution TheoryMigration Deconvolution Theory

Migration Green’s function Migration Green’s function (Schuster and Hu, 2000)(Schuster and Hu, 2000)

)|( 0rrmig

MD efficiently eliminates the blurring influence in the migration image

ooomig rdrRrrrm

)()()(

Migration Green’s functionMigration Green’s function(Schuster and Hu, 2000)(Schuster and Hu, 2000)

sgsoogsgomig rdrdrrGrrGrrGrrGrr

)()()()()( **

Migration Deconvolution TheoryMigration Deconvolution Theory

MD Implementation Steps:MD Implementation Steps:

Step 1: Prepare traveltime table

Acquisition geometry information

Velocity

Step 2: Calculate the migration Green’s function at the depth Zi ),,,,( ippj zyxzyx

)(~.

)(~

)(~

),,~,(~

...,),,,~,(~

),,,~,(~...

),,~,(~

...,),,,~,(~

),,,~,(~

),,~,(~

...,),,,~,(~

),,,~,(~

)(

.

)(~)(~

2

1

21

22212

12111

2

1

nnppnppnppn

npppppp

npppppp

n zkR

zkR

zkR

zyxzkzyxzkzyxzk

zyxzkzyxzkzyxzk

zyxzkzyxzkzyxzk

zkm

zkm

zkm

For the discrete MD equation:

FFT of ),,,,( ippj zyxzyx

MigratedMigrated

datadata

m

True True ReflectivityReflectivity

R

Step 4: Obtain MD image at the depth Zi by solving following equation

Rm

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

Step 6: IFFT of MD image from wavenumber domain to space domain

Motivation

OutlineOutline

3-D Migration Deconvolution (MD)

Examples

Conclusions

3-D Land Field Data

: Rec

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: Rec

eiver

: sourc

e

: sourc

e

Mig (Courtesy of Unocal) MDInline Number1 90 1 90

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ber

Inline Number

(2 kft)

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Dep

th (

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8

1 310Crossline Number (Inline =50)

Fault

Fault

(3.08 kft)

Inline Number1 90 1 901

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Inline Number

Mig MD

(3.6 kft)

Inline Number1 90 1 901

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sslin

e N

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Inline Number

Mig MD

Inline Number1 901.1

7.0

Dep

th (

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)

90 Inline Number1

Mig (courtesy of Unocal) MD

(Crossline=50)

Inline Number1 901.1

7.0

Dep

th (

kft

)

90 Inline Number1

(Crossline=50)

Mig (courtesy of Unocal) MD

2.5

1.8

Mig in Inline (Courtesy of Unocal) MDT

imes

(s)

(crossline 200)

1 90 1 901.1

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Dep

th (

kft

)Mig (courtesy of Unocal) MD

Inline Number Inline Number

(crossline 200)

1 90 1 901.1

8.0

Dep

th (

kft

)Mig (courtesy of Unocal) MD

Inline Number Inline Number

1 15.51.1

8.0

Dep

th (

kft

)Y (kft) (X=11.2 kft)

Mig

MD

Motivation

OutlineOutline

Migration Deconvolution (MD)

Examples

Conclusions

Implementation of MD

ConclusionsConclusionsImprove resolution and suppress migration artifacts

MD cost is related to acquisition geometry

Little improvement in the deeper part of migration image

Thanks ToThanks To• Aramco, Unocal, and Chevron-Texaco Aramco, Unocal, and Chevron-Texaco

for providing the field datafor providing the field data

• 2003 UTAM sponsors2003 UTAM sponsors

• Alan Leeds (ChevronTexaco), and Alan Leeds (ChevronTexaco), and George Yao (Unocal)George Yao (Unocal)

• CHPCCHPC