Computational approach to integrate 3D X-ray microtomography and NMR data.

Area of Science:

• Geophysics
• Materials Science
• Computational Physics

Background:

• Nuclear Magnetic Resonance (NMR) is crucial for studying fluid dynamics in porous media.
• Analyzing complex pore structures and molecular diffusion presents significant challenges.
• Existing theoretical models for confined diffusion have limitations, necessitating computational approaches.

Purpose of the Study:

• To develop a statistical approach correlating surface magnetic relaxivity with NMR relaxation in digital porous media.
• To elucidate the relationship between simulated relaxation times and pore size.
• To simulate one- and two-dimensional NMR techniques, including relaxation times (T1, T2) and diffusion coefficients (D).

Main Methods:

• Utilizing the Random Walk Method within a Digital Porous Medium framework.
• Incorporating the Bergman model for magnetic relaxation considering surface interactions.
• Employing 3D X-ray microtomography to create realistic digital rock models.
• Simulating translational diffusion to preserve microstructural complexity.

Main Results:

• The proposed method successfully simulates NMR relaxation times (T1, T2) and diffusion coefficients (D).
• Validation against ideal spherical pores and the Brownstein-Tarr model showed good agreement.
• Comparison of simulated and experimental results for synthetic porous media demonstrated method efficacy.

Conclusions:

• The computational method provides a robust tool for analyzing NMR data in complex porous materials.
• This approach enhances the understanding of the relationship between pore size and NMR relaxation.
• Computational physics shows significant potential for advancing NMR analysis in geosciences and materials science.