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Prediction of Nuclear Magnetic Resonance Surface Relaxivity for Shales
Jiuhui Cheng1,2, Shuitao Zhang2,3, Xiaolong Song1
1State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining, Anhui University of Science & Technology, Huainan 232001, China.
Abstract:
Surface relaxivity is a key parameter for the inversion of pore size distribution via nuclear magnetic resonance measurements. This paper aims to predict the surface relaxivity of shales based on other available experimental data, including chemical elements, minerals, and magnetic susceptibility. The partial least-squares regression method is conducted by mineralogy and chemical elements, and their root-mean-square errors are 0.01 μm/s and 0.05 μm/s, respectively. The regression coefficients show significant influence from several elements, such as iron and titanium. The effect of the magnetic characteristics of shales on the surface relaxivity is better illustrated by mineralogy, especially magnesium in clay minerals. This issue can be attributed to the magnetic susceptibility of shales. The results reveal a strong positive correlation between magnetic susceptibility and surface relaxivity. In summary, exploring the determinants of surface relaxivity can speed up and simplify the current calibration.
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