A machine learning method for quantitative characterization of multifluid components based on NMR T1-T2 spectra in shale

Author:

Gu Mingxuan,Xie RanhongORCID,Jin Guowen,Guo JiangfengORCID

Funder

Science Foundation of China University of Petroleum, Beijing

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Publisher

Elsevier BV

Reference27 articles.

1. A review of the current progress of CO2 injection EOR and carbon storage in shale oil reservoirs;Jia;Fuel,2019

2. Reconstruction of LWD-NMR T2 water spectrum and fluid recognition based on microscopic pore structure constraints;Yang;Geoenergy Sci Eng,2023

3. Fractal behaviors of NMR saturated and centrifugal T2 spectra in oil shale reservoirs: the Paleogene Funing formation in Subei basin;Liu;China Mar Petrol Geol,2021

4. Dunn KJ, Bergman DJ, LaTorraca GA. Nuclear magnetic resonance: Petrophysical and logging applications. Oxford. U.K. 2002.

5. Comprehensive characterization of petrophysical properties in shale by solvent extraction experiments and 2D NMR;Xu;Fuel,2023

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