Low-field nuclear magnetic resonance
(NMR) is widely used for accurate
characterization of coal pore structure. The NMR T
2 spectrum represents the pore size distribution. Also,
the T
2 cutoff (T
2c) value is a key parameter, reflecting the free/bound fluid
proportion of coal. To characterize the pore structure of coal more
comprehensively, the NMR T
2 spectra of
coals with different pore structures were characterized by multifractal,
and then, the T
2c values were predicted
by a BP neural network model. The main conclusions are as follows:
the T
2 spectra of three ranks of coals
(anthracite, bituminous coal, and lignite) showed typical unimodal,
bimodal, and trimodal distributions, respectively. The porosity had
a weak negative correlation with T
2c,
whereas the proportion of free fluid had a strong negative correlation
with T
2c. The quality indices τ(q) of the three coals changed monotonously, which conformed
to the multifractal characteristics. The generalized fractal dimension
spectra decreased in an inverse S-shape, decreasing while the multifractal
singular spectra were hook-shaped. D
min, ΔD, αmax, α0, and Δα showed strong positive correlations with T
2c, which indicated that with an increase in T
2c, the proportion of large-size pores decreased
and the local pore size distribution became more concentrated and
inhomogeneous. The predicted T
2c values
of the training, verification, and test sets of the BP neural network
model fitted the measured T
2c values well,
and the mean square error was only 0.17%. The trained BP neural network
model was reliable and can be used for the T
2c prediction of more similar coal samples.
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