Abstract

Nuclear spin diffusion in the susceptibility-contrast induced internal field (DDIF–NMR) has been recently exploited to probe pore sizes in sedimentary rock. This method has hitherto been applied to only a few samples and its ability to discern the entire spectrum of pore length scales actually present has not been fully validated against independent methods. Motivated by the need to measure the pore size distribution in vuggy carbonate rocks exhibiting structure at disparate length scales, ranging from several nanometers (matrix pores) to millimeters (vugs), we carry out a DDIF–NMR study of real and synthetic vuggy porous media. The synthetic media have controlled amounts of matrix and vuggy porosity and are made by first sintering and dissolution of known proportions of glass beads and much larger calcium carbonate particles. DDIF–NMR studies are complemented by single-point magnetic resonance imaging (SPI–MRI), which provides independent determination of the vug size distribution, mercury porosimetry and statistical image analysis (SIA) of large (2.6-cm wide), high-resolution (1.87 μm/pixel) images of thin-sections. The two-point correlation function determined in the μm-to-mm range from images is used to simulate small-angle neutron scattering, and the computed scattering intensity is extended (where appropriate) in the large- Q range using the surface fractal scaling law I ( Q ) ∝ Q D s − 6 , with D s estimated from mercury porosimetry. The extended I( Q) data are interpreted in terms of a poly-dispersed spherical pore model, allowing the determination of the pore size distribution in the nm-to-mm range. Pore size distributions obtained by statistical image analysis are found to be consistent with mercury retraction capillary pressure data. Pore size distributions from DDIF–NMR data show reasonable agreement to SIA results for samples without vuggy porosity (i.e., pore diameter < 200 μm). For other samples, significant differences are observed, underscoring the need to develop more sophisticated models for the interpretation of DDIF–NMR data.

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