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Methods and limitations of NMR data inversion for fluid typing.

Boqin Sun1, Keh-Jim Dunn

  • 1ChevronTexaco Energy Technology Company, San Ramon, CA 94583, USA.

Journal of Magnetic Resonance (San Diego, Calif. : 1997)
|June 9, 2004
PubMed
Summary

Two new 1D Nuclear Magnetic Resonance (NMR) inversion methods extract fluid saturations efficiently. These techniques offer faster fluid typing and are useful when 2D NMR data is insufficient.

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Area of Science:

  • Geophysics
  • Petrophysics
  • Nuclear Magnetic Resonance

Background:

  • Nuclear Magnetic Resonance (NMR) is crucial for characterizing fluid properties in porous media.
  • Traditional 2D NMR inversion methods can be data-intensive and time-consuming.
  • Extracting fluid saturations and properties from limited NMR data remains a challenge.

Purpose of the Study:

  • To develop and present two novel 1D NMR inversion methods for fluid saturation extraction.
  • To provide faster alternatives to 2D NMR for fluid typing and analysis.
  • To address limitations in NMR inversion methods, particularly concerning fluid typing ambiguity.

Main Methods:

  • Introduced two 1D NMR inversion techniques varying echo spacing and wait time.
  • Method 1: Used a shift matrix to link individual fluid T2 distributions to the apparent T2 distribution.

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  • Method 2: Employed a global evolution matrix for CPMG echo trains, considering T1, T2 relaxation, and diffusion.
  • Main Results:

    • Successfully extracted fluid saturations and T2 distributions by minimizing differences between model and measured data.
    • Demonstrated the utility of these methods for scenarios with insufficient data for 2D NMR.
    • Highlighted the speed advantage of these 1D methods over 2D approaches for fluid typing.

    Conclusions:

    • The developed 1D NMR inversion methods provide efficient tools for fluid saturation analysis.
    • These methods are valuable alternatives when 2D NMR is not feasible or too slow.
    • Acknowledged inherent ambiguities in NMR fluid typing due to matrix singularities, suggesting prior knowledge can mitigate this.