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Related Experiment Videos

The inversion of NMR log data sets with different measurement errors.

K J Dunn1, G A LaTorraca

  • 1Chevron Petroleum Technology Company, La Habra, California 90633, USA.

Journal of Magnetic Resonance (San Diego, Calif. : 1997)
|September 10, 1999
PubMed
Summary

This study introduces a composite-data processing method for handling multiple datasets with varying measurement errors. The method improves the accuracy of singular value decomposition inversion and log analysis.

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Area of Science:

  • Geophysics
  • Data Science
  • Signal Processing

Background:

  • Simultaneous processing of multiple datasets with differing measurement errors is challenging.
  • Singular value decomposition (SVD) inversion is sensitive to noise levels and cutoff criteria.
  • Accurate analysis of geophysical logs requires robust data processing techniques.

Purpose of the Study:

  • To develop and present a composite-data processing method for handling multiple datasets with different measurement errors.
  • To investigate the impact of noise levels and cutoff criteria on SVD inversion within this composite method.
  • To demonstrate the utility of the method through processed log examples and the apparent T(1)/T(2) ratio.

Main Methods:

  • Development of a composite-data processing framework for simultaneous analysis of multiple datasets.

Related Experiment Videos

  • Examination of singular value decomposition (SVD) inversion sensitivity to noise and cutoff selection.
  • Application of the method to geophysical log data and analysis of extracted parameters.

    • Main Results:

    • The composite-data processing method effectively integrates datasets with varying measurement errors.
    • Noise level and cutoff criteria significantly influence SVD inversion accuracy and solution uncertainty.
    • The apparent T(1)/T(2) ratio derived from processed logs shows potential utility in data interpretation.

    Conclusions:

    • The presented composite-data processing method offers a robust approach for analyzing multi-dataset logs with differing errors.
    • Understanding noise and cutoff effects is crucial for reliable SVD inversion in composite data analysis.
    • The method and derived ratios enhance geophysical log interpretation capabilities.