Effect of rock composition and texture on pore size distributions in shales: applications in low field nuclear magnetic resonance

Abstract

There are various methods to assess the pore size distribution (PSD) of porous materials; amongst all, NMR is the only technique that can be utilized for subsurface applications. The key parameter to transform NMR time domain response to PSD size domain data is surface relaxivity. The common practice is to consider a constant surface relaxivity throughout a well, formation or rock type regardless of the variations in rock compositions; this results in inaccurate PSD estimation using NMR log data. In this thesis I established a methodology to calculate the surface relaxivity in shales considering the rock composition and texture. I present the steps to achieve this goal in three steps: (a) Understanding the challenges of NMR acquisition, analysis and interpretation in shales, (b) Measuring the porosity, PSD and surface area and providing a practice to check the reliability of these measurements in shales, (c) Developing a methodology to calculate the surface relaxivity honoring the variations paramagnetic mineral content, susceptibility, distribution and texture. Application of NMR in unconventional rocks requires adjustment of NMR data acquisition and analysis to the unique properties of these rocks such as high level of heterogeneity, complex pore structure, fine grains, and presence of nano-scale pores. Identifying these challenges improves our understanding of NMR response in shales and increases the quality of the acquired and analyzed data. Calculation of surface relaxivity, as a measure of how fluids and rock surfaces react, requires reliable measurement of different petrophysical properties of the rock such as porosity, total specific surface area, and PSD using other techniques. I studied the reliability of different techniques to measure these petrophysical properties for shales by performing a thorough comparative study of porosity and PSD for different shale formations. The result of my study showed that clay type and content, total organic carbon (TOC), and thermal maturity are the main factors that affect the reliability of a measurement technique in organic rich shales. The final step for surface relaxivity measurement is to combine the mentioned petrophysical measurement with NMR data and investigating the effect of rock composition and texture on surface relaxivity. The surface relaxivities were calculated for organic rich samples with different thermal maturity and also shales with no organic content. My results show that identification of paramagnetic minerals that affect the surface relaxivity, their content and distribution are the key factors that affect the surface relaxivity of the rock. In absence of ferromagnetic minerals, paramagnetic clays such as chlorite, illite and illite-smectite mixed layer are the main mineral groups that affect the surface relaxivity. Since clays are one of the controlling factors of rock quality and gamma ray logs respond to clays occurring in oil and gas producing formations, these logs can be used to help perform a more accurate NMR log interpretation.