Applied Magnetic Resonance

, Volume 50, Issue 9, pp 1099–1123 | Cite as

An Improved NMR Permeability Model for Macromolecules Flowing in Porous Medium

  • Lin Wang
  • Lizhi XiaoEmail author
  • Yan Zhang
  • Guangzhi Liao
  • Linlin Wang
  • Wenzheng Yue
Original Paper


The extraction of macromolecules from nano-self-assembled material can be used as a laboratory model for enhancing oil recovery in reservoirs. By combining Darcy’s law and Poiseuille equation, an improved nuclear magnetic resonance (NMR) permeability model, suitable for macromolecular flow in mesopores is obtained. The calibration coefficients in the Coates equation are expressed in terms of the physical parameters of pore throat ratio rb/rt, tortuosity, and thickness of bond film in the improved model. The results show that the proportion of irreducible fluid to total fluid obtained through NMR characterization can reflect the variation tendency of irreducible macromolecule and water. By simplifying the pores of the extracted samples, the thickness model of irreducible macromolecule and water is established with the total thicknesses calculated as 1.482 nm, 1.585 nm, 1.674 nm, and 1.834 nm. The corresponding permeability results obtained from the NMR characterization (KNMR) are 7.39 mD, 6.02 mD, 5.27 mD, and 6.25 mD. The permeability results obtained from mercury intrusion experiment (KHG) are 5.10 mD, 4.73 mD, 5.82 mD, and 5.56 mD, and those from the Darcy flow experiment (KD) are 4.1 mD and 5.19 mD. The absolute deviation between KNMR and KHG varies from 0.69 to 2.29 mD, while that between KNMR and KD is 1.58 mD. This method can be applied to the enhanced recovery of shale oil.



This work was supported by the National Natural Science Foundation of China (21427812) and the “111 Project” Discipline Innovative Engineering Plan, China (B13010). Major national R&D projects (2016ZX05019-002-008).

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  • Lin Wang
    • 1
  • Lizhi Xiao
    • 1
    • 2
    Email author
  • Yan Zhang
    • 1
  • Guangzhi Liao
    • 1
  • Linlin Wang
    • 1
  • Wenzheng Yue
    • 1
  1. 1.State Key Laboratory of Petroleum Resources and ProspectingChina University of PetroleumBeijingChina
  2. 2.Harvard SEAS-CUPB Joint LaboratoryCambridgeUSA

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