Energy Science & Engineering (Apr 2020)

A method to determine nuclear magnetic resonance T2 cutoff value of tight sandstone reservoir based on multifractal analysis

  • Yunbing Hu,
  • Yinghai Guo,
  • Junjian Zhang,
  • Jingwen Shangguan,
  • Mi Li,
  • Fangkai Quan,
  • Guanlin Li

DOI
https://doi.org/10.1002/ese3.574
Journal volume & issue
Vol. 8, no. 4
pp. 1135 – 1148

Abstract

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Abstract Nuclear magnetic resonance (NMR) T2 cutoff value is an important parameter for pore structure evaluation. It is complicated and uneconomical to obtain T2 cutoff value by an experimental method; therefore, it is necessary to explore a prediction method of T2 cutoff value. In this paper, 10 samples of tight gas reservoirs in the eastern Ordos Basin were selected, and then saturation and centrifugal experiments of nuclear magnetic resonance were carried out. On this basis, multifractal theory was introduced to calculate the multifractal characteristics of the NMR T2 spectrum of each sample, and the relationship between multifractal parameters and T2 value was analyzed. The influencing factors of the T2 cutoff value were clarified, and the prediction model of the T2 cutoff value was constructed accordingly. The results show that the T2 spectra of sandstones in the study area can be divided into three types: single steeple peak, double steeple peak, and irregular double peak. The pore diameter of the three types is 1 nm ~ 3×104 nm, 1 nm ~ 104 nm and 1 nm ~ 4×103 nm, respectively. The T2 cutoff value ranges from 9.72 to 35.16 ms. The correlation analysis suggests that the symmetrical fractal dimension difference and symmetrical multifractal dimension ratio (Dmin−Dmax, Dmin/Dmax) shows a positive linear correlation with the T2 cutoff value. The value of T2 cutoff gradually decreases with the increase of the flow zone indicator (FZI). Therefore, three parameters, including symmetrical fractal dimension difference, symmetrical multifractal number ratio, and FZI are optimized, and the prediction model for the NMR T2 cutoff value of sandstone samples in the study area is proposed. The introduction of porosity‐related parameters compensates for the shortcomings of previous T2 cutoff value prediction models. At the same time, the prediction model is proven to be accurate and reliable by testing the measured data of the samples near the study area. The results of this paper can be used for further study of the NMR T2 cutoff value prediction of tight sandstone reservoirs in different areas.

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