Least squares migration of japex data and pemex data
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Least Squares Migration of JAPEX Data and PEMEX Data. Naoshi Aoki. Outline. Theory LSM resiliency to artifacts from poor acquisition geometry LSM image sensitivity to wavelet estimation errors Multi-scale LSM applied to poststack JAPEX data

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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


Outline

  • 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


Theory

Forward modeling

Poststack 2D Syncline Model

Kirchhoff Migration

Inversion

LSM

Steepest descent algorithm

Ricker wavelet (15 Hz)


Outline

  • 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 Resiliency to Artifacts from Poor Acquisition Geometry

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 vs. LSMApplied to the 3D U Model

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


LSM Resiliency to Artifacts

  • Test Summary

    • LSM showed a significant resiliency to artifacts from poor acquisition geometry.

    • LSM has an ability to reduce data acquisition expense.


Outline

  • 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 Image Sensitivity to Wavelet Estimation Errors

  • 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

LSM Image with Correct Source Wavelet

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 with a Ricker Wavelet (15 Hz)

LSM Image

0

0

Depth (km)

Depth (km)

2

2

0

0

2

2

X (km)

X (km)


LSM Image Sensitivity to Errors in the Source Wavelet

  • 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.


2D Poststack Data from Japan Sea

JAPEX 2D SSP marine data description:

Acquired in 1974,

Dominant frequency of 15 Hz.

0

TWT (s)

5

0

20

X (km)


Multi-scale LSM

  • 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.


Multi-scale LSM applied to JAPEX 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 vs. Kirchhoff Migration

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)


Resolution comparison

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)


Outline

  • 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


PEMEX 3D OBC Data from GOM

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 vs. Kirchhoff Migration from PEMEX Data IL3100

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)


Resolution comparison

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


TO LSM Applied for 3D Data

Preliminary Result of LSM Image after 4 iterations

Kirchhoff Migration Image


Conclusions

  • Numerical results show:

    • LSM has a significant resilience to artifacts from poor acquisition geometries .

    • an accurate waveletestimate provides an accurate LSM image.

  • Results from JAPEX and PEMEX data show:

    • 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.


  • Future work

    • GOAL: 3D LSMin less than 10 iterations.

      • Further improvement in efficiency will be investigated.


    Acknowledgements

    • 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.


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