Improve Migration Image Quality Improve Migration Image Quality by 3-D Migration Deconvolutionby 3-D Migration Deconvolution

Jianhua Yu, Gerard T. Schuster Jianhua Yu, Gerard T. Schuster

University of Utah University of Utah

Motivation

OutlineOutline

Migration Deconvolution

Examples

Conclusions

Implementation of MD

Migration noise and artifacts

Seismic Migration NoiseSeismic Migration Noise0.5

3.5

Dep

th (

km

)

Footprint

Weak illumination

Limit recording aperture

What Affects Seismic Migration Quality What Affects Seismic Migration Quality

Irregular acquisition geometry

Incorrect velocity

High order phenomenon: anisotropy, attenuation etc.

Bandlimited wavelet

Noise and artifacts

Migration Image suffers fromMigration Image suffers from

Poor spatial resolution

Non-uniform illumination

Objective :

Develop 3-D migration deconvolution

Limit recording aperture

Irregular acquisition geometry

to deblur the influence of

Objective :

Improving spatial resolution

Enhancing illumination

Suppressing migration noise and artifacts

Motivation

OutlineOutline

Migration Deconvolution (MD)

Examples

Conclusions

Implementation of MD

mm = = G dG dTT butbut dd = G = G RRMigrated Section Data

G RG R

mm = = PSF(R)PSF(R) Migration image = Blurred image of Migration image = Blurred image of

true reflectivity modeltrue reflectivity model

Migration operator

Migration Deconvolution TheoryMigration Deconvolution Theory

mRG GT

Migration imageReflectivity

Migration Green’s function

Migration Deconvolution TheoryMigration Deconvolution Theory

mRG GT

G GT -1

][ G GT -1

][

11

Migration Deconvolution TheoryMigration Deconvolution Theory

mR

G GT -1

][

1

Migration Deconvolution TheoryMigration Deconvolution Theory

sgsoogsgomig rdrdrrGrrGrrGrrGrr

)()()()()( **Migration Green’s functionMigration Green’s function

(Schuster et al., 2000)(Schuster et al., 2000)

ooomig rdrRrrrm

)()()(

sgsoogsgomig rdrdrrGrrGrrGrrGrr

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

Migration Deconvolution TheoryMigration Deconvolution Theory

Lateral shift invariant migration Green’s functionLateral shift invariant migration Green’s function

Reduction of MD cost

),( pp yx --- --- Reference position of migration Green’s functionReference position of migration Green’s function

),,,,( oppoomig zyxzyyxx

oooooo dzdydxzyxR ),,(

)(rm

In wavenumber-space domain:

Rm

Motivation

OutlineOutline

Migration Deconvolution (MD)

Examples

Conclusions

Implementation of MD

MD Implementation Steps:MD Implementation Steps:

Step 1: Prepare traveltime table

Velocity cube

Acquisition geometry information

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

Step 3: Obtain MD image at the depth Zi by solving following equation

Rm

MD Implementation Steps:MD Implementation Steps:

Step 4: Repeat Steps 2-3 until the maximum depth is finished

Motivation

OutlineOutline

Migration Deconvolution (MD)

Examples: Synthetic data

Conclusions

Implementation of MD

Recording Geometry

: Sources : Sources : Receivers: Receivers

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

VSP Geometry: source 21 x 21; geophone: 12

Depth=1.75 kmMigration MD

GOM Velocity Model X (km)

Dep

th (

km

)

12

0

2 10

X (km)

Dep

th (

km

)

10

8

4 10 X (km)4 10

Migration Migration+MD

X (km)

Dep

th (

km

)

10

8.5

4 10 X (km)4 10

Migration Migration+MD

00

12.2 km12.2 km

00

3-D SEG/EAGE Salt Model

12.2 km12.2 km

9 x 5 Sources; 9 x 5 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 z = 1.4 km MDX (km)

3

10

Y (

km

)

5 9.8 5 9.8X (km)

Mig (z=1.2 km)X (km)

3

10

Y (

km

)

5 9.8 5 9.8X (km)

MD (z=1.2 km)

X (km)0 203

10

Dep

th (

km

)Sigsbee2B Model

X (km)0 202.5

10

Dep

th (

km

)

Mig

X (km)0 202.5

10

Dep

th (

km

)

MD

5

10

Dep

th (

km

)Mig.

MD

Motivation

OutlineOutline

Migration Deconvolution (MD)

Examples: 2-D field data

Conclusions

Implementation of MD

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) PSTMD

MD

Tim

e (s

)Mig (courtesy of Aramco)

Tim

e (s

)Mig (Courtesy of Aramco) MD

Mig (Courtesy of Aramco) MD

Motivation

OutlineOutline

Migration Deconvolution (MD)

Examples: 3-D field data

Conclusions

Implementation of MD

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

3.0

Mig in Inline (Courtesy of Unocal) MDT

imes

(s)

1.2

Mig (Courtesy of Unocal) MDInline Number1 90 1 90

1

300

Cro

sslin

e N

um

ber

Inline Number

(2 kft)

(3.6 kft)

Inline Number1 90 1 901

265

Cro

sslin

e N

um

ber

Inline Number

Mig MD

Inline Number1 901.1

7.0

Dep

th (

kft

)

90 Inline Number1

(Crossline=50)

Mig (courtesy of Unocal) MD

(crossline 200)

1 90 1 901.1

8.0

Dep

th (

kft

)Mig (courtesy of Unocal) MD

Inline Number Inline Number

Motivation

OutlineOutline

Migration Deconvolution (MD)

Examples

Conclusions

Implementation of MD

ConclusionsConclusions

Suppress migration noise

Improve spatial resolution

MD cost is related to acquisition geometry

V(z) assumption for moderately complex models

AcknowledgementsAcknowledgements

UTAM Sponsors

SMAART Joint Venture

Aramco,Aramco, BP and Unocal