AI Article Synopsis

  • - Nuclear magnetic resonance (NMR) relaxometry is used to estimate porosity and pore sizes in petroleum resources but faces challenges in accurately analyzing shales due to overlapping solid and fluid signals.
  • - Traditional analysis methods, like the inverse Laplace transform, can produce unrealistic results for data with Gaussian decays, leading to overestimated signals and inaccurate relaxation times.
  • - The new simultaneous Gaussian-Exponential (SGE) inversion method offers improved accuracy for analyzing NMR data, yielding more realistic results while maintaining reliability across different fields like material, medical, and food sciences.

Article Abstract

Nuclear magnetic resonance (NMR) relaxometry is commonly used to provide lithology-independent porosity and pore-size estimates for petroleum resource evaluation based on fluid-phase signals. However in shales, substantial hydrogen content is associated with solid and fluid signals and both may be detected. Depending on the motional regime, the signal from the solids may be best described using either exponential or Gaussian decay functions. When the inverse Laplace transform, the standard method for analysis of NMR relaxometry results, is applied to data containing Gaussian decays, this can lead to physically unrealistic responses such as signal or porosity overcall and relaxation times that are too short to be determined using the applied instrument settings. We apply a new simultaneous Gaussian-Exponential (SGE) inversion method to simulated data and measured results obtained on a variety of oil shale samples. The SGE inversion produces more physically realistic results than the inverse Laplace transform and displays more consistent relaxation behavior at high magnetic field strengths. Residuals for the SGE inversion are consistently lower than for the inverse Laplace method and signal overcall at short T2 times is mitigated. Beyond geological samples, the method can also be applied in other fields where the sample relaxation consists of both Gaussian and exponential decays, for example in material, medical and food sciences.

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Source
http://dx.doi.org/10.1016/j.jmr.2014.10.015DOI Listing

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