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3-D Migration DeconvolutionPowerPoint Presentation

3-D Migration Deconvolution

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3-D Migration Deconvolution

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

Jianxing Hu, GXT

Bob Estill, Unocal

Why Do Migration Deconvolution (MD) ?

Migration Deconvolution

Implementation of MD

Examples

Conclusions

Why Do Migration Deconvolution (MD) ?

Migration Deconvolution

Implementation of MD

Examples

Conclusions

Footprint

Weak illumination

Migration noise and artifacts

0

Depth (km)

3.5

Improving spatial resolution

Enhancing illumination

Purpose of MD Processing:

Suppressing migration noise and artifacts

Why Do Migration Deconvolution (MD) ?

Migration Deconvolution

Implementation of MD

Examples

Conclusions

L is modeling operator

Reflectivity

Migrated image

Migration:

T

M = L

L R

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

Why Do Migration Deconvolution (MD) ?

Migration Deconvolution

Implementation of MD

Examples

Conclusions

Acquisition geometry information

Velocity cube

MD Implementation Steps:

Step 1:

Prepare traveltime table

or

Use migration timetable

Y (km)

N

Depth Leveli

L

Depth (km)

MD Implementation Steps:

Step 2:

Calculate the migration Green’s function

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:

Why Do Migration Deconvolution (MD) ?

Migration Deconvolution

Implementation of MD

Examples : Synthetic data

Conclusions

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

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

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

3.5 km

5 X 1 Sources; 11 X 7 Receivers

Meandering Stream Model

2.5 km

2.5 km

0

0

0

X (km)

2.5

2.5

0

Y (km)

Z=3.5 KM

Model

MD

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

3-D SEG/EAGE Salt Model

Y=7.12 km

Y (km)

X (km)

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

Mig (z=1.2 km)

MD (z=1.2 km)

X (km)

X (km)

5

9.8

5

9.8

3

Y (km)

10

Mig (z=1.2 km)

MD (z=1.2 km)

Why Do Migration Deconvolution (MD) ?

Migration Deconvolution

Implementation of MD

Examples: 2-D field data

Conclusions

X (km)

0

6

0

Time (s)

8

X (km)

0

6

0

PSTMD

PSTM(courtesy of Unocal)

Time (s)

8

X (km)

0

6

3

PSTM(courtesy of Unocal)

PSTMD

Time (s)

8

Why Do Migration Deconvolution (MD) ?

Migration Deconvolution

Implementation of MD

Examples: 3-D field data

Conclusions

: Sources

: Receivers

3-D Land Field Data

3D PSTM (courtesy of Unocal)

MD

Inline

Crossline

1.6 s

MD

3D PSTM (courtesy of Unocal)

Crossline

2.0 s

Mig in Inline (Courtesy of Unocal) MD

3

Mig

MD

Mig

MD

MD

Mig (Courtesy of Unocal)

Inline Number

Inline Number

1

90

1

90

1

Crossline Number

300

(2 kft)

Fault

Fault

MD

Inline Number

Inline Number

1

90

1

90

1

Crossline Number

265

Mig (Courtesy of Unocal)

(3.6 kft)

Mig (courtesy of Unocal)

MD

Inline Number

1

90

1

Inline Number

90

1.1

Depth (kft)

7.0

(Crossline=50)

Mig (courtesy of Unocal)

MD

1

90

1

90

1.1

Depth (kft)

8.0

(crossline 200)

Crossline Number

(Inline =50)

1

250

1.1

Depth (kft)

Mig (Unocal)

7.0

1.1

MD

7.0

Why Do Migration Deconvolution (MD) ?

Migration Deconvolution

Implementation of MD

Examples

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

- 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