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Nuclear Magnetic Resonance Characterization of Petrophysical Properties in Tight Sandstone Reservoirs
Author(s) -
Jin Guowen,
Xie Ranhong,
Xiao Lizhi
Publication year - 2020
Publication title -
journal of geophysical research: solid earth
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.983
H-Index - 232
eISSN - 2169-9356
pISSN - 2169-9313
DOI - 10.1029/2019jb018716
Subject(s) - petrophysics , permeability (electromagnetism) , saturation (graph theory) , geology , porosity , mineralogy , nuclear magnetic resonance , geotechnical engineering , chemistry , physics , mathematics , biochemistry , combinatorics , membrane
Abstract Permeability and bound water saturation ( S wb ) are key parameters reflecting the petrophysical properties of porous rocks. Nuclear magnetic resonance (NMR) has proved to be effective in investigating the properties of porous media. However, estimating S wb and the permeability in tight sandstone reservoirs based on conventional NMR methods, which requires the inversion of NMR echo data to obtain the transverse relaxation time ( T 2 ) distribution, proves to be a challenging task. In this study, a method is proposed to estimate S wb and the permeability in tight sandstone reservoirs based on the direct analysis of NMR echo data, thus avoiding the inversion process. A total of 20 tight sandstone samples from the Ordos Basin in China was taken for laboratory NMR measurements. The kernel function of the echo data was used to characterize the ratio distribution of bound water volume to pore volume for different pore sizes. Following this, an echo data calibration method was applied to estimate S wb without the inversion of the T 2 distribution, and the window method was used to reduce the impact of noise in the echo data. Furthermore, models for estimating permeability were proposed based on the determined windows of the NMR echo data in the S wb estimation. The reliability of the proposed method for estimating S wb and permeability was verified by comparing the estimated and experimental results. Our study provides an efficient method for the estimation of petrophysical parameters in porous rocks based on the direct analysis of NMR echo data.