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 pore-scale network representations designed to overcome such limitations. We present a novel capillary network representation with an enhanced level of spatial detail at microscale. We find that the network-based 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.
Highlights
Permeability is the key parameter for quantifying fluid flow in porous rocks
We show in the following that a high-accuracy, network-based representation of a microscopic fraction of a rock’s connected pore space is a suitable template for computationally predicting experimental permeability results obtained from the same rock sample at lab scale, a volume upscaling by 3 orders of magnitude
For evaluating the influence of pore scale representation quality on the accuracy of permeability predictions, we have used three candidate network representations with varying levels of spatial detail as geometrical template: (1) the Capillary Network Model (CNM), which transforms the pore space into a voxel-wide line at the center of the pore channels; (2) the Reduced Max Ball Model (RMB)[30], in which the network is constructed using connecting cylinders modelled by longer line segments that follow the medial axis of the pore space; and (3) the Pore Network Model (PNM)[31], which divides pore space into pores and throats, where each pore is a node in the network and each throat is a link between nodes
Summary
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. We show in the following that a high-accuracy, network-based representation of a microscopic fraction of a rock’s connected pore space is a suitable template for computationally predicting experimental permeability results obtained from the same rock sample at lab scale, a volume upscaling by 3 orders of magnitude. G. cylinders and spheres, for which flow properties are typically obtained in (semi-) analytical form[14,20,24] In any of these methods, the tradeoff between the voxel (volume pixel) size and the total imaged sample volume poses practical limits to the spatial resolution of lab scale samples in which permeability measurements are typically performed. By aggregating the data of all samples studied, we have obtained the scaling relationships and slopes for characterizing more broadly the permeability of the entire sandstone sample class
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