Theoretical Mechanism and Application of Sphere–Cylinder Model in NMR for Oil–Water Porous Media

Liu, Tangyan; Yan, Jun; Yan, Jun; Feng, Xiaoqiang; Ma, Zaitian

doi:10.1007/s00024-011-0395-y

Cited by 4 publications

(1 citation statement)

References 9 publications

(20 reference statements)

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“…NMR experiments can effectively detect non-connected pores and nano-scale pores in core samples [22]. Analyzing the distribution and peak positions of the transverse relaxation time, T 2 , enables the determination of pore types and their size distribution.…”

Section: Introductionmentioning

confidence: 99%

Utilizing Differences in Mercury Injection Capillary Pressure and Nuclear Magnetic Resonance Pore Size Distributions for Enhanced Rock Quality Evaluation: A Winland-Style Approach with Physical Meaning

Gu,

Wang,

Guo

et al. 2024

Applied Sciences

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Pore structure is a fundamental parameter in determining the hydrocarbon storage capacity and flow characteristics of a reservoir. Mercury injection capillary pressure (MICP) and nuclear magnetic resonance (NMR) are two commonly utilized techniques for characterizing rock pore structures. However, current studies indicate that disparities in testing methodologies due to distinct physical characteristics lead to a partial misalignment in pore size distributions. We conducted MICP (dynamic) and NMR (static) experiments on eight tight sandstone and eight shale samples and proposed a method to utilize information from the differences in MICP and NMR pore size distributions, aiming to enhance the accuracy of rock quality analysis. We observed that in rock cores where large pores are interconnected with smaller pore throats, MICP tends to overestimate the proportion of these smaller pores and underestimate the larger ones. Furthermore, we integrated information from both dynamic and static experimental processes based on physical significance and found that the fitting accuracy of the newly proposed method is superior to the Winland r35 equation. Compared to the Winland r35 equation, our new method significantly improves fitting accuracy, increasing the R-squared value from 0.46 to 0.93 in sandstones and from 0.80 to 0.87 in shales. This represents a potential high-precision, comprehensive tool for rock quality analysis, offering a new perspective for an in-depth understanding of rock properties.

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Section: Introductionmentioning

confidence: 99%