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 japex data and pemex data

Least Squares Migration of JAPEXData and PEMEX Data

Naoshi Aoki


Outline

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

Theory

Forward modeling

Poststack 2D Syncline Model

Kirchhoff Migration

Inversion

LSM

Steepest descent algorithm

Ricker wavelet (15 Hz)


Outline1

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

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 lsm applied to the 3d u model

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

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.


Outline2

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 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).


Lsm image with correct source wavelet

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)


Lsm image with a ricker wavelet 15 hz

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

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

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

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

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 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

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)


Outline3

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

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 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 comparison1

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

TO LSM Applied for 3D Data

Preliminary Result of LSM Image after 4 iterations

Kirchhoff Migration Image


Conclusions

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

    Future work

    • GOAL: 3D LSMin less than 10 iterations.

      • Further improvement in efficiency will be investigated.


    Acknowledgements

    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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