11 Sandstones: raw, filtered and segmented data

• https://doi.org/10.1038/s41598-021-90090-0
• https://doi.org/10.1038/s41598-021-90090-0
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Abstract — Permeability is the key parameter for quantifying fluid flow in porous rocks. Knowledge of the spatial distribution of the connected pore space allows, in principle, to predict the permeability of a rock sample. However, limitations in feature resolution and approximations at microscopic scales have so far precluded systematic upscaling of permeability predictions. Here, we report fluid flow simulations in capillary network representations designed to overcome such limitations. Performed with an unprecedented level of accuracy in geometric approximation at microscale, the pore scale flow simulations predict experimental permeabilities measured at lab scale in the same rock sample without the need for calibration or correction. By applying the method to a broader class of representative geological samples, with permeability values covering two orders of magnitude, we obtain scaling relationships that reveal how mesoscale permeability emerges from microscopic capillary diameter and fluid velocity distributions.

• https://doi.org/10.1016/j.petrol.2020.107400
• 10.1016/j.petrol.2020.107400

Abstract — Many of the world's oil reservoirs are sandstones and some equations have been widely explored to estimate their permeability from NMR measurements, especially those based on the Schlumberger Doll Research (SDR) and Timur–Coates models. In both cases, the permeability is assumed to be related to pore sizes. Brownstein and Tarr showed that, under fast diffusion regime, the NMR transverse relaxation time is proportional to the pore size and the proportionality constant is defined by the interaction fluid/surface and represented by the surface relaxivity ρ. However, although there are several methods in the literature to estimate ρ, no standard methods have been established so far, to our knowledge. In this work, we estimated surface relaxivity by combining experimental and simulated NMR data using a recently developed computational method to emulate the NMR signal of fluids within Digital Rocks. In addition, we analyzed the correlation between the pore radii and the experimentally measured gas permeability. Comparing Schlumberger and Timur-Coates models for permeability prediction, we propose that the macro and microporosity intensities identified in the T2 distribution are correlated with characteristics of the sandstone surface. Thus, sandstone surface relaxivity can be estimated using the ratio of macro and microporosity intensities from NMR and, consequently, the pore size distribution can be obtained using only T2 distribution data.