Reservoir and lithofacies shale classification based on NMR logging

Hongyan Yu, Zhenliang Wang, Fenggang Wen, Chinareza Rezaee, Maxim Lebedev, Xiaolong Li, Yihuai Zhang, Stefan Iglauer

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
97 Downloads (Pure)


Shale gas reservoirs have fine-grained textures and high organic contents, leading to complex pore structures. Therefore, accurate well-log derived pore size distributions are difficult to acquire for this unconventional reservoir type, despite their importance. However, nuclear magnetic resonance (NMR) logging can in principle provide such information via hydrogen relaxation time measurements. Thus, in this paper, NMR response curves (of shale samples) were rigorously mathematically analyzed (with an Expectation Maximization algorithm) and categorized based on the NMR data and their geology, respectively. Thus the number of the NMR peaks, their relaxation times and amplitudes were analyzed to characterize pore size distributions and lithofacies. Seven pore size distribution classes were distinguished; these were verified independently with Pulsed-Neutron Spectrometry (PNS) well-log data. This study thus improves the interpretation of well log data in terms of pore structure and mineralogy of shale reservoirs, and consequently aids in the optimization of shale gas extraction from the subsurface.

Original languageEnglish
Pages (from-to)202-209
Number of pages8
JournalPetroleum Research
Issue number3
Early online date10 May 2020
Publication statusPublished - Sept 2020


  • Composition
  • NMR logging
  • Pore size distribution
  • Shale gas

ASJC Scopus subject areas

  • Geochemistry and Petrology
  • Geology
  • Energy Engineering and Power Technology


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