Pore-scale velocities in three-dimensional porous materials with trapped immiscible fluid

Abstract

We study and document the influence of wetting and nonwetting trapped immiscible fluid on the probability distribution of pore-scale velocities of the flowing fluid phase. We focus on drainage and imbibition processes within a three-dimensional microcomputed tomographic image of a real rock sample. The probability distribution of velocity magnitude displays a heavier tail for trapped nonwetting than wetting fluid. This behavior is a signature of marked changes in the distribution and strength of preferential flow paths promoted by the wettability property of the trapped fluid. When the latter is wetting the host solid matrix, high-velocity areas initially present during single-phase flow conditions are mainly characterized by increased or decreased velocity magnitudes, and the velocity field remains correlated with its counterpart associated with the single-phase case. Otherwise, when the trapped fluid is nonwetting, features that are observed to prevail are appearance and disappearance of high-velocity areas and a velocity field that is less correlated to the one obtained under single-phase conditions.

Upscaling Mixing-Controlled Reactions in Unsaturated Porous Media

“…This assumption is valid because of the low flow rate Q used, which also prevents the deformation and displacement of air bubbles. Guédon et al (2019) found no differences in the velocity distributions for slip and no-slip boundary conditions between wetting and non-wetting phases. Triadis et al (2019) found the transport behavior is the same for slip and no-slip boundary conditions between water and air phase.…”

Implementation of Three-Phase Black-Oil Reservoir Models Assisted by Micro-Scale Analyses

“…Here, we present Steady-State (SS) two-phase coreflooding experiments (Moghadasi et al, 2015) as well as the methodology for the evaluation of two-phase relative permeabilities based on the use of porescale simulations (Guédon et al, 2019). We also describe the technique for the synthetic generation of heterogeneous fields (see Section 2.3) employed in our reservoir simulations.…”

“…When considering the results of direct pore-scale two-phase flow simulations (i.e., our numerical approach), we evaluate uncertainties on reservoir model input parameters through a standard Maximum Likelihood (ML) framework grounded on results from computational fluid dynamics (CFD) simulations on three-dimensional (3-D) models (Guédon et al, 2019).…”

“…The latter employs a finite volume discretization of the Navier-Stokes equations and a Volume-of-Fluid technique to track interfaces between fluid phases. The numerical settings employed follow those implemented in our previous studies (Guédon et al, 2019). A Corey-type (Corey and Rathjens, 1956) two-phase relative permeability model is calibrated (through a ML approach, as detailed in Ranaee et al, 2016; to modeling results.…”

A seemingly universal particle kinetic distribution in porous media

“…Understanding the particles' kinetic probability distribution in real porous media is essential to achieve particulate control 8,9 . Prior studies described how such distributions depend on pore structural variables (e.g., obstacle size 10 , porosity [11][12][13] , pore structure 14 ) by fitting the probability data into standard probability distribution functions (PDFs), such as exponential 10,15,16 , stretched exponential 11,17 , power-law 14,18 , and power-exponential ones 12 . However, the assessment of their predictive ability remains elusive, because these functions contain fitting parameters that often lack a solid physical foundation.…”