Characterization of the pore structure of tight sandstone reservoirs
carries great significance for the evaluation of the reservoir storage
and transport properties, “sweet spots” prediction,
and reservoir development. In this paper, the pore structures of tight sandstone reservoirs
are characterized on the basis of the multifractal analysis of nuclear
magnetic resonance (NMR) transverse relaxation time (T
2) distributions. For the T
2 distribution models established using the mixed Gaussian distribution
function, the characteristic parameters of the high probability measure
areas (the right branch of the generalized fractal dimension spectrum
and the left branch of the singularity spectrum) are closely related
to the model parameters (weight coefficients and standard deviations
of the short relaxation component). For the T
2 distributions of the tight sandstone samples, the results
of the relationships between the multifractal characteristic parameters
of the T
2 distributions and the petrophysical
parameters of the samples indicate that the characteristic parameters
of the high probability measure areas (α
max
, α0 – α
max
, D
max
, and D
0 – D
max
) are closely related to the permeability, T
2 geometric mean (T
2lm
), and T
35 (the T
2 value at 35% saturation in the normalized reverse accumulated T
2 distribution curve) values, which can be used
to quantitatively evaluate the pore structure heterogeneity of tight
sandstones. These samples can be classified into four types on the
basis of the shape of the T
2 distribution
and the petrophysical parameters. Substantial differences exist among
the multifractal characteristics of the different sample types, and
several parameters (α
max
, α0 – α
max
, D
max
, and D
0 – D
max
) can be used to classify the sample types. The multifractal
characteristic parameters are closely related to the clay mineral
content. As the clay mineral content increases, the heterogeneity
of the high probability measure areas of the T
2 distribution increases. The results of the multifractal analysis
of the NMR logging data further demonstrate the effectiveness of evaluating
the pore structure of tight sandstone reservoirs based on the multifractal
characteristics of the NMR T
2 distribution.
Permeability and bound water saturation (Swb) 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 Swb 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 (T2) distribution, proves to be a challenging task. In this study, a method is proposed to estimate Swb 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 Swb without the inversion of the T2 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 Swb estimation. The reliability of the proposed method for estimating Swb 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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.