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Least Squares Migration of JAPEX Data and PEMEX Data

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Least Squares Migration of JAPEXData and PEMEX Data

Naoshi Aoki

- Theory
- LSM resiliency to artifactsfrom poor acquisition geometry
- LSM image sensitivity to wavelet estimation errors
- Multi-scale LSM applied to poststack JAPEX data
- Target-oriented LSM applied to poststack PEMEX data
- Conclusions

Forward modeling

Poststack 2D Syncline Model

Kirchhoff Migration

Inversion

LSM

Steepest descent algorithm

Ricker wavelet (15 Hz)

- Theory
- LSM resiliency to artifacts from poor acquisition geometry
- LSM image sensitivity to wavelet estimation errors
- Multi-scale LSM applied to poststack JAPEX data
- Target-oriented LSM applied to poststack PEMEX data
- Conclusions

3D U Model

Model Description

Model size:

1.8 x 1.8 x 1.8 km

U shape reflectivity anomaly

Cross-spread geometry

Source : 16 shots, 100 m int.

Receiver : 16 receivers , 100 m int.

0

CSG

TWT (s)

● Source

● Receiver

5

0

1.8

X (m)

U model is designed for testing Prestack 3D LSM with arbitrary 3D survey geometry.

Kirchhoff Migration Images

(a) Actual Reflectivity

(c) Z = 250 m

(e) Z = 750 m

(g) Z=1250m

LSM Images after 30 Iterations

(b) Test geometry

(d) Z=250m

(f) Z=750m

(h) Z=1250m

● Source

● Receiver

- Test Summary
- LSM showed a significant resiliency to artifacts from poor acquisition geometry.
- LSM has an ability to reduce data acquisition expense.

- Theory
- LSM resiliency to artifacts from poor acquisition geometry
- LSM image sensitivity to wavelet estimation errors
- Multi-scale LSM applied to poststack JAPEX data
- Target-oriented LSM applied to poststack PEMEX data
- Conclusions

- LSM algorithm requires a source wavelet.
- I tested LSM image sensitivity to wavelet estimation errors in the following 2 cases :
- LSM with correct wavelet,
- LSM with a Ricker wavelet (15 Hz).

Actual Model

Data

LSM Image

0

0

0

Depth (km)

Depth (km)

TWT (s)

2

2

2

0

0

0

2

2

2

X (km)

X (m)

X (km)

Kirchhoff Migration Image

Actual Model

LSM Image

0

0

Depth (km)

Depth (km)

2

2

0

0

2

2

X (km)

X (km)

- Test Summary
- An accurate estimate of the source wavelet is important to obtain an accurate LSM image.
- However, LSM images are usually better than the standard migration image.

JAPEX 2D SSP marine data description:

Acquired in 1974,

Dominant frequency of 15 Hz.

0

TWT (s)

5

0

20

X (km)

- Starts by estimating a low wavenumber reflectivity model in order to avoid getting trapped in a local minimum.
- Band-pass filters, where the frequency bandwidth increases with the number of iterations, were iteratively applied to the input data.

X10 5

MS LSM Image

Multi-scale (MS) LSM vs. Standard LSM

Convergence Curves

Standard LSM Image

Multi-scale LSM

3.0

0.7

0.7

Standard LSM

20Hz

Depth (km)

Residual

25

30

32

34

36

38

40

1.9

0.5

1.9

0

40

2.4

4.9

Iteration

2.4

4.9

X (km)

X (km)

LSM Image

Kirchhoff Migration Image

0.7

0.7

Depth (km)

Depth (km)

1.9

1.9

4.9

4.9

2.4

2.4

X (km)

X (km)

LSM vs. Standard Migration

Magnitude Spectrum of Migration Image

1

LSM Image

Kirchhoff Migration Image

0.7

0.7

Magnitude

Depth (km)

Depth (km)

0

0

0.04

1.2

1.2

Wavenumber (1/m)

4.3

4.3

3.7

3.7

X (km)

X (km)

- Theory
- LSM resiliency to artifacts from poor acquisition geometry
- LSM image sensitivity to wavelet estimation errors
- Multi-scale LSM applied to poststack JAPEX data
- Target-oriented LSM applied to poststack PEMEX data
- Conclusions

Acquired in1990s.

Since acquisition geometry is sparse, noise is dominant in the shallowpart.

IL3100 Stacked Section

0

TWT (s)

4

1001

1

XL Number

LSM Image

Kirchhoff Migration Image

0.7

0.7

Depth (m)

Depth (m)

1.9

1.9

4.9

2.4

2.4

4.9

X (m)

X (m)

LSM

LSM vs. Standard Migration

Magnitude Spectrum of Migration Image

Kirchhoff Migration

1

LSM Image

Kirchhoff Migration Image

1

Magnitude

Depth (km)

0

0

650

551

0.04

2.2

XL Number

Wavenumber (1/m)

650

551

XL Number

Preliminary Result of LSM Image after 4 iterations

Kirchhoff Migration Image

- Numerical results show:
- LSM has a significant resilience to artifacts from poor acquisition geometries .
- an accurate waveletestimate provides an accurate LSM image.

- faster convergence rate is provided by a multi-scale migration scheme.
- 2D LSM is a practical means for improving quality image.
- Encouraging results for TO LSM obtained from the 3D data subset.

- GOAL: 3D LSMin less than 10 iterations.
- Further improvement in efficiency will be investigated.

- We thank PEMEX Exploration and Production for permission to use and publish its Gulf of Mexico data.
- I would like to thank JOGMEC and JAPEX for supporting my study at the University of Utah.
- We also thank the UTAM consortium members for supporting my work.