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3-D Migration Deconvolution. Jianhua Yu, University of Utah Gerard T. Schuster, University of Utah. Jianxing Hu, GXT. Bob Estill, Unocal. Outline. Why Do Migration Deconvolution (MD) ?. Migration Deconvolution. Implementation of MD. Examples.

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3 d migration deconvolution
3-D Migration Deconvolution

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

Jianxing Hu, GXT

Bob Estill, Unocal

outline
Outline

Why Do Migration Deconvolution (MD) ?

Migration Deconvolution

Implementation of MD

Examples

Conclusions

outline1
Outline

Why Do Migration Deconvolution (MD) ?

Migration Deconvolution

Implementation of MD

Examples

Conclusions

migration noise problems

Footprint

Weak illumination

Migration noise and artifacts

Migration Noise Problems

0

Depth (km)

3.5

slide5

Improving spatial resolution

Enhancing illumination

Purpose of MD Processing:

Suppressing migration noise and artifacts

outline2
Outline

Why Do Migration Deconvolution (MD) ?

Migration Deconvolution

Implementation of MD

Examples

Conclusions

slide7

L is modeling operator

Reflectivity

Migrated image

Migration:

T

M = L

L R

slide8

T

-1

R = (L L ) M

Goal:

Reflectivity

Migrated Section

Design an improved MD filter

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

3-D PRESTACK MD

outline3
Outline

Why Do Migration Deconvolution (MD) ?

Migration Deconvolution

Implementation of MD

Examples

Conclusions

slide10

Acquisition geometry information

Velocity cube

MD Implementation Steps:

Step 1:

Prepare traveltime table

or

Use migration timetable

slide11

Y (km)

N

Depth Leveli

L

Depth (km)

MD Implementation Steps:

Step 2:

Calculate the migration Green’s function

slide12

Step 5:

Repeat Steps 2-4 until the maximum depth is finished

Step 4:

Invert MD image at the depth Zi by solving linear equations

MD Implementation Steps:

outline4
Outline

Why Do Migration Deconvolution (MD) ?

Migration Deconvolution

Implementation of MD

Examples : Synthetic data

Conclusions

slide14

10 km

3X3 Sources;

11 X 11 Receivers

dxshot=dyshot=1.5 km

dxg=dyg=0.3 km

3-D Point Scatterer Model

3 km

3 km

0

0

Imaging: dx=dy=50 m

dz=100 m

slide15

0

0

0

0

0

0

0

0

0

0

0

0

X (km)

X (km)

X (km)

X (km)

X (km)

X (km)

Y (km)

Y (km)

Y (km)

Y (km)

Y (km)

Y (km)

3

3

3

3

3

3

3

3

3

3

3

3

MIG MD

Depth Slices

Z=1 km

Z=3 km

Z=5 km

slide16

0

0

0

0

0

0

0

0

0

0

0

0

X (km)

X (km)

X (km)

X (km)

X (km)

X (km)

Y (km)

Y (km)

Y (km)

Y (km)

Y (km)

Y (km)

3

3

3

3

3

3

3

3

3

3

3

3

MIG MD

Depth Slices

Z=7 km

Z=9 km

Z=10 km

slide17

3.5 km

5 X 1 Sources; 11 X 7 Receivers

Meandering Stream Model

2.5 km

2.5 km

0

0

slide18

0

X (km)

2.5

2.5

0

Y (km)

Z=3.5 KM

Mig

Model

MD

slide19

9 X5 Sources;

201 X 201 Receivers

dxshot=dyshot=1 km

3-D SEG/EAGE Salt Model

12.2 km

12.2 km

0

0

Imaging: dx=dy=20 m

slide20

3-D SEG/EAGE Salt Model

Y=7.12 km

Y (km)

X (km)

slide21

Mig and MD ( z=1.4 km, negative polarity)

X (km)

X (km)

5

9.8

5

9.8

3

Y (km)

10

Mig

MD

slide22

Mig (z=1.2 km)

MD (z=1.2 km)

X (km)

X (km)

5

9.8

5

9.8

3

Y (km)

10

slide23

Mig (z=1.2 km)

MD (z=1.2 km)

outline5
Outline

Why Do Migration Deconvolution (MD) ?

Migration Deconvolution

Implementation of MD

Examples: 2-D field data

Conclusions

slide26

X (km)

0

6

0

PSTMD

PSTM(courtesy of Unocal)

MD

Time (s)

8

slide27

X (km)

0

6

3

PSTM(courtesy of Unocal)

PSTMD

MD

Time (s)

8

outline6
Outline

Why Do Migration Deconvolution (MD) ?

Migration Deconvolution

Implementation of MD

Examples: 3-D field data

Conclusions

slide29

: Sources

: Receivers

3-D Land Field Data

slide30

3D PSTM (courtesy of Unocal)

MD

Inline

Crossline

1.6 s

slide31

MD

3D PSTM (courtesy of Unocal)

Crossline

2.0 s

slide33

Mig

MD

Mig

MD

slide34

MD

Mig (Courtesy of Unocal)

Inline Number

Inline Number

1

90

1

90

1

Crossline Number

300

(2 kft)

slide35

Fault

Fault

slide36

MD

Inline Number

Inline Number

1

90

1

90

1

Crossline Number

265

Mig (Courtesy of Unocal)

(3.6 kft)

slide37

Mig (courtesy of Unocal)

MD

Inline Number

1

90

1

Inline Number

90

1.1

Depth (kft)

7.0

(Crossline=50)

slide38

Mig (courtesy of Unocal)

MD

1

90

1

90

1.1

Depth (kft)

8.0

(crossline 200)

slide39

Crossline Number

(Inline =50)

1

250

1.1

Depth (kft)

Mig (Unocal)

7.0

1.1

MD

7.0

outline7
Outline

Why Do Migration Deconvolution (MD) ?

Migration Deconvolution

Implementation of MD

Examples

Conclusions

conclusions

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

Conclusions
acknowledgments
Acknowledgments
  • Thank Amramco, Unocal, and Chevron-Texaco for providing the data sets
  • The help and comments from Alan Leeds and George Yao are very appreciated
  • Thank 2002 UTAM sponsors for their financial support
  • http://utam.gg.utah.edu
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