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Comparison of Poststack MD Depth SlicesPowerPoint Presentation

Comparison of Poststack MD Depth Slices

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Comparison of Poststack MD Depth Slices

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- X (km)

- X (km)

- 10

- 10

- 8

- 8

- 6

- 6

4

4

- 6

- 6

- Y (km)

- Y (km)

- 8

- 8

- Kirchhoff Image

- MD Image

- 4

- 4

- 6

- 6

- 8

- 8

- 10

- 10

- 1

- 1

- Depth (km)

- Depth (km)

- 4

- 4

- X (km)

- Prestack Kirchhoff Migration Image of
- a North Sea Data Set

- X (km)

- MD Image

Jianxing Hu

University of Utah

- Methodology
- Theory and implementation
- Numerical Tests
- Synthetic and field data tests
- Conclusions

Forward Modeling:

Model Space

Green’s Function

Reflectivity

Wavelet

Seismic data

Migration:

Data Space

Migrated Image

Seismic Data

- Relation of Migrated Image and Reflectivity Distribution

Model Space

Where:

Data Space

Denote

as the migration Green’s Function

Model Space

Model Space

Model

Space

--- reference position of migration Green’s function

Traveltime Table

Migration

Green’s function

Calculate migration Green’s function

Recording geometry &

migrated image dimension

+

Velocity Model

Apply migration deconvolution

filter to the stacked prestack

migration image

RTM

RTM

6

6

5

5

1

1

2

2

Depth (km)

Depth (km)

3

3

Offset(km)

Offset(km)

Deconvolved Image

Migration Image

5

Pseudo-Convolution

Recording Geometry &

migrated image dimension

+

Prestackmigration

Green’s function

Zero-offset trace location &

migrated image dimension

+

Velocity Model

Traveltime Table

Poststackmigration

Green’s function

Entire Migrated

Image Cube

Division Parts

Image Layers

Y

X

Z

Partitioned

Image Cube

Smaller Traveltime Table

Computing Nodes

Partitioned

Image Cube

Smaller Traveltime Table

Computing Nodes

Problem: Lose Far-Offset Traces

Subdivide the migration image area and use multi-

reference migration Green’s function to account for

lateral velocity variation and far-field artifacts

Multi-Reference migration Green’s function

- Methodology
- Numerical Tests
- Conclusions

- 3-D point scatterer model
- 3-D meandering stream model
- 2-D SEG/EAGE overthrust model
- 2-D Husky data set (Canadian Foothills)
- 3-D SEG/EAGE salt model
- 3-D West Texas data set

Recording Geometry

5 X 5 Sources; 21 X 21 Receivers

Wavelet frequency 50 Hz

(0, 1km)

(0, 0)

(1km, 1km)

(1km, 0)

Point scatterer

Prestack KM vs. Prestack MD

Y

X

Y

Y

X

X

Y

X

Prestack KM vs. Poststack MD

Y

X

Y

Y

X

X

Y

X

- 3-D point scatterer model
- 3-D meandering stream model
- 2-D SEG/EAGE overthrust model
- 2-D Husky data set (Canadian Foothills)
- 3-D SEG/EAGE salt model
- 3-D West Texas data set

Recording Geometry

5 X 5 Sources; 21 X 21 Receivers

Wavelet frequency 50 Hz

(0, 1 km)

(0, 0)

(1 km,1 km)

(1 km, 0)

A river channel

Meandering River Model

X (m)

0

1000

0

Depth (m)

1000

X (m)

0

1000

0

Depth (m)

1000

X (m)

0

1000

0

Depth (m)

1000

- 3-D point scatterer model
- 3-D meandering stream model
- 2-D SEG/EAGE overthrust model
- 2-D Husky data set (Canadian Foothills)
- 3-D SEG/EAGE salt model
- 3-D West Texas data set

- 0 km

20 km

0 km

4 km

- Prestack Migration Image

X(km)

0 km

- 20 km

0 km

Depth (km)

4 km

- Deconvolved Migration Image

X(km)

Depth (km)

Zoom View of KM and MD

3

3

7

7

X (km)

X (km)

2

2

Depth (km)

Depth (km)

3

3

4

4

Prestack KM

Prestack MD

- 3-D point scatterer model
- 3-D meandering stream model
- 2-D SEG/EAGE overthrust model
- 2-D Husky data set (Canadian Foothills)
- 3-D SEG/EAGE salt model
- 3-D West Texas data set

X(km)

10

0

5

0

2

Depth (km)

6

4

X(km)

10

0

5

0

2

Depth (km)

6

7000

Velocity (m/s)

3200

X(km)

10

0

5

0

2

Depth (km)

6

X(km)

10

0

5

0

2

Depth (km)

6

KM Image

MD Image

3 references

MD Image

20 references

X(km)

10

0

5

0

2

Depth (km)

6

A

5

9

X(km)

1

KM

Depth (km)

3

5

X(km)

9

1

MD

Depth (km)

3

X(km)

10

0

5

0

2

Depth (km)

6

B

11

14

X(km)

1

KM

Depth (km)

3

11

X(km)

14

1

MD

Depth (km)

3

X(km)

10

0

5

0

2

Depth (km)

6

C

10

X(km)

14

2

KM

Depth (km)

5

10

X(km)

14

2

MD

Depth (km)

5

10

X(km)

14

2

KM

Depth (km)

Whitening &

Bandpass

5

10

X(km)

14

2

MD

Depth (km)

5

- 3-D point scatterer model
- 3-D meandering stream model
- 2-D SEG/EAGE overthrust model
- 2-D Husky data set
- 3-D SEG/EAGE salt model
- 3-D West Texas data set

Y (km)

5

8

0

0

Depth (km)

2

2

4

4

Y (km)

5

8

KM Inline (97,Y) Section

MD Inline (97,Y) Section

X (km)

X (km)

8

8

11

11

0

0

2

2

4

4

Depth (km)

KM Crossline (X,97) Section

MD Crossline (X,97) Section

Depth Slices

Y (km)

Y (km)

5

5

8

8

8

8

X (km)

X (km)

11

11

KM

MD

Y (km)

5

8

Y (km)

5

8

8

8

600 m

X (km)

X (km)

11

11

800 m

- 3-D point scatterer model
- 3-D meandering stream model
- 2-D SEG/EAGE overthrust model
- 2-D Husky data set
- 3-D SEG/EAGE salt model
- 3-D West Texas data set

X (kft)

0

15

0

20

Velocity (kft/s)

Depth(kft)

14

6

X (kft)

0

10

4

8

Depth (kft)

KM Inline

Section (X,93)

12

16

X (kft)

0

10

4

8

Depth (kft)

12

16

KM Crossline

Section (93,Y)

KM

MD

X(kft)

0

3

4

4

Depth (kft)

6

6

8

8

X(kft)

0

3

KM

MD

X(kft)

X(kft)

4

4

2

2

4

Depth (kft)

6

8

4

6

8

- Methodology
- Numerical Tests
- Conclusions

More work is needed to remedy

the problems in MD for 3-D

land prestack data

Standard post-migration

processing procedure ?

Works well on 2-D land and 3-D

synthetic marine prestack data

- Thank 1999 UTAM sponsors for their financial support