Transport in Porous Media最新文献

Applications of porous media are increasing in various scientific disciplines, such as energy storage or conversion systems and giant capacitors. It has been recognized that the transport and reaction processes occurring within pores significantly influence the performance of porous media, while transport phenomena at the pore scale are not well-characterized. The experimental effective diffusion coefficient of naphthalene-nitrogen was studied using a regression method based on a radial dimension transient model of diffusion in the porous medium. The weight of samples containing naphthalene was continuously recorded by a digital precision balance. This study was performed at three different temperatures: 303.15 K (30 °C), 323.15 K (50 °C), and 343.15 K (70 °C) at atmospheric pressure, with porous metal matrices of 90% porosity containing different amounts of naphthalene in a laminar nitrogen stream. The fitting of the diffusion equation in spherical coordinates and the weight changes provided the mass transfer coefficients; as a result, a power correlation to estimate the effective diffusion coefficient was obtained.

Evaporation from a porous medium partially saturated with saline water, causes the salinity (salt concentration) to increase near the top of the porous medium as water leaves while salt stays behind. As the density of the water increases with increased salt concentration, the evaporation leads to a gravitational unstable setting, where density instabilities can form. Whether density instabilities form, depends on a large range of parameters like the evaporation rate and intrinsic permeability of the porous medium, but also on the water saturation. As water saturation decreases, the storage, convection and diffusion of salt also decrease, which all influence the onset of instabilities. By performing a linear stability analysis on the governing equations, we give criteria for onset of instabilities, with a particular focus on impact of saturation. While decreased storage and diffusion make onset of instabilities more unstable, decreased convection has a stabilizing effect on the onset of instabilities. We find that their combined influence is that lower saturation overall gives earlier onset times. Numerical simulations give information about the further development of these instabilities. With this knowledge we can predict whether and when density instabilities occur, and how they will influence the further development of salt concentration in the porous medium.

Advanced digital rock technology using X-ray microtomography (micro-CT) has been applied to investigate three-phase pore occupancies in rock samples. These three-phase experiments involved different injection sequences in water-wet, oil-wet and mixed-wet rocks under immiscible and near miscible conditions using carbon dioxide or nitrogen as injected gases. In this work, the process-based theory of van Dijke et al., which has been previously used in both capillary bundle (CB) and 3D-lattice pore network models (PNM), is applied to simulate all the available published three-phase pore occupancy data. The predicted three-phase pore occupancies from the CB model are compared to the experimental results. The three-phase theory first uses the physical parameters as reported by the original authors; namely, the three values of interfacial tension (σ_ow, σ_go, and σ_gw), wetting conditions and the distribution of oil–water contact angle where a single contact angle is assigned to each pore. However, there are some uncertainties in these quantities, and to achieve a good match some reasonable adjustment of the input data is usually required. For water-wet systems, the best agreement with the measured pore occupancy is achieved with only minor parameter adjustments: for example, a change of 2 mN/m in gas-oil interfacial tension and a decrease in gas–water interfacial tensions to represent true three-phase equilibrium conditions. For oil-wet and mixed-wet systems, adjusted contact angle distributions, within the experimental uncertainty, are also required to match the data. Both the experimental results and modelling predictions show that three-phase displacements are the same at immiscible (gas-oil interfacial tensions of around 10 mN/m or more) and near-miscible (gas-oil interfacial tensions of approximately 1 mN/m) conditions for water-wet cases, while in oil-wet systems, miscibility affects the wetting order of gas and water. Water becomes the most non-wetting phase at near-miscible conditions and gas is intermediate-wet. The model developed in this work also helps (i) to interpret the physical processes in the experiment and (ii) to determine the likely equilibrium values of interfacial tension and contact angles.

The present work contributes to advancing the theoretical understanding of ferroconvection phenomena in porous media, which is critical for engineering applications involving magnetic nanofluids, such as biomedical cooling, energy systems, and microfluidics. In this work, we investigate convective heat transfer and subcritical dynamics in rotating ferrofluids with couple stresses, incorporating local thermal non-equilibrium effects (LTNE). The study employs a two-temperature model to describe heat exchange between solid and liquid phases and the Darcy-Brinkman model for ferrofluid flow in porous media. Stability and convection onset are analyzed under free-free boundary conditions using linear and nonlinear methods, with the Galerkin technique solving the resulting eigenvalue problems. To enhance the predictive capabilities of the study, a hybrid Artificial Neural Network (ANN) model was developed, trained using key parameters (magnetization, couple stresses, permeability, rotation, porosity-modified conductivity ratio, and interface heat transfer coefficient) as inputs and corresponding Rayleigh numbers as outputs. Rayleigh number differences reveal a significant subcritical region. The results indicated that increasing magnetization, permeability, and porosity-modified conductivity ratio reduced the critical Rayleigh number, thus destabilizing the system. Conversely, higher couple stresses, and interphase heat transfer coefficient delayed the onset of convection and stabilized the system. Furthermore, the subcritical region was notably expanded under strong couple stress. The ANN demonstrated high accuracy ((R^2 = 0.999287)) in predicting Rayleigh numbers, closely matching analytical results. This hybrid approach offers novel insights into ferrofluid convection dynamics under LTNE conditions and highlights the interplay between thermal and flow control mechanisms.

Unconventional resources, such as shale and tight-gas sandstone reservoirs, have been one of the key components of energy supplies in North America. Although research on tight and ultra-tight rocks has made significant progress, there exist questions about their physical and hydraulic properties that remain unanswered. For instance, the distribution of pore throats and its mathematical form in unconventional reservoir rocks are still unknown. The main objective of this study, therefore, is to investigate the potential probability density function representing the pore-throat size distribution in tight gas sandstones. For this purpose, we analyzed more than 100 Mesaverde tight gas sandstone samples collected from Western US basins under ambient and confining pressure conditions. We converted the measured capillary pressure (S_w-P_c) curves into the pore-throat size distributions by plotting (Delta) S_w/(Delta) ln(P_c) versus log-transform pore-throat diameter, log(d), and observed non-Gaussian trends for most samples. More specifically, we detected a heavily tailed (left-skewed) distribution, in contrast to mostly observed trends in conventional reservoir rocks. For the first time, we applied the generalized normal probability density function to characterize the log-transformed pore-throat size distributions and demonstrated that its unimodal and/or bimodal forms fit the distributions from tight gas sandstones reasonably well. For the ambient samples, the generalized normal distribution was the corrected Akaike information criterion (AIC_c) preferred model in 56% of cases, followed by the bimodal generalized normal (28%), bimodal normal (12%), and normal (4.2%) distributions. For the confined samples, the generalized normal distribution was the AIC_c preferred model in 46% of cases, followed by the bimodal generalized normal (32%), bimodal normal (18%), and normal (3.8%) distributions. For both unimodal and bimodal samples, we found that the median of the pore-throat size distribution was correlated to the logarithm of porosity (with (R>0.63)) and to the logarithm of permeability (with (R>) 0.74) for which the correlations were stronger. Results also showed that the (log)permeability was exponentially correlated to the porosity with R² = 0.83 for the samples under the ambient conditions and R² = 0.82 for the samples under the confined conditions.

The study of couple stress nanofluids with variable thermal conductivity incorporating Soret and Dufour effects is carried out in this article. Couple stress fluids, defined by the rotational behavior of material elements under load, enhance modeling accuracy by capturing microstructural effects at small scales. This improves the solution of complex boundary value problems and deepens understanding of heat and mass transfer in physiological flows. The current model demonstrates improved performance, as evidenced by enhancements in the Nusselt number and increases in the Sherwood number compared to conventional nanofluid models, as summarized in the tables. The governing flow equations, which involve multiple independent variables, are reduced to a single variable using suitable similarity transformations and solved using MATLAB’s built-in BVP4c package and the homotopy analysis method (HAM). For specified values of the thermophoresis parameter, increasing the number of iterations in HAM enhances its accuracy, yielding results more comparable to those reported in the literature than the shooting method. Both methods demonstrate excellent agreement, with an error margin of less than 0.001, and the convergence for the series solution is also obtained. Additionally, the effects of thermophoresis and Brownian motion on flow behavior are explored in graphical analysis.

Understanding hyporheic processes is key to support stream health and functioning. The present study addresses the impact of weak buoyancy effects on hyporheic exchange. In this context, we focus on the density-driven processes on pore scale, which had not been investigated so far to our knowledge. For that purpose, the transport of an active scalar across the interface between a turbulent free-flow region and a random sphere pack with macroscopically flat surface was investigated by pore-resolved single-domain direct numerical simulation. At a permeability Reynolds number of (Re_K = 1.63) and a friction Reynolds number of (Re_tau = 173), seven simulation cases were evaluated with Rayleigh–Darcy numbers varying within the range of (Ra_textrm{D} in [-20, 400]), where (Ra_textrm{D} < 0) represents stabilising and (Ra_textrm{D} > 0) destabilising buoyancy effects. While turbulent scalar transport dominates the well-mixed free-flow region, dispersive transport contributes considerably to the scalar flux within the sphere pack, even in the absence of buoyancy effects. The latter observation implies that the velocity and scalar field must be spatially heterogeneous. Under these conditions, even weak destabilising buoyancy effects can enhance spatial variations, strengthen preferred scalar advection paths, and increase the hyporheic flux. A stabilising stratification of the scalar field has the opposite effect and reduces the vertical exchange within the porous medium. We conclude that even though weak buoyancy effects do not introduce genuine density-driven instabilities, they have a clear quantitative impact on hyporheic exchange with far-reaching ecological implications.

Interwell partitioning tracer tests (IPTTs) are conducted in mature oil fields to estimate remaining oil in place, which is crucial for subsequent economic analyses and decisions regarding further field development. An IPTT involves the simultaneous injection of two types of tracers: conservative and partitioning, that probe the aqueous and oil phases, respectively. Although this test requires time, it probes the entire fluid flow path, not just the near-wellbore area, as is the case with other methods such as single-well tests. Accurate interpretation of interwell tracer test data is of critical importance for the oil and gas industry. Published IPTT case studies lack physical justification for the choice of tracer flow models. In this study, we provide such justifications along with guidelines for selecting appropriate tracer flow models. First, we review existing models for the transport of partitioning and conservative tracers and demonstrate their applicability range based on mass conservation analysis. Based on this analysis, we propose a refined model of partitioning tracer flow with Robin boundary conditions that accounts for non-equilibrium partitioning. Such analysis is missing in the literature. Next, we illustrate errors in estimating remaining oil if an inappropriate model is used for data interpretation. Notably, the choice of an incorrect model can lead to either underestimation or overestimation of the remaining oil, with the latter being of greater financial concern. Finally, we apply the non-equilibrium partitioning model to a published IPTT dataset from a layered carbonate reservoir and compare our remaining oil estimates with results of the original study. To the best of our knowledge, analysis of such cases with non-equilibrium partitioning has not been documented in the literature.

Bio-clogging is a prevalent phenomenon in nature, impacting numerous engineering projects. The concentration of salt in a solution has a significant effect on microbial clogging processes, leading to alterations in the permeability of porous media. This study explores the influences mechanisms of solution salinity on the microbial clogging in porous media at the pore scale. We utilized Lattice Boltzmann model with immersed boundary (LBM–IMB) to simulate the flow field and solute transport in porous media while the cellular automaton model was employed to simulate microbial growth. Firstly, the biofilm growth kinetics model was validated under hydrostatic conditions. Secondly, the parameters of microbial growth characteristics were determined under varying NaCl concentrations by experiments. Finally, we examined the effects of NaCl concentration on microbial growth, spatial heterogeneity, flow field and concentration field, and permeability in porous media. The main results are as follows: (1) Microbial growth exhibits heterogeneity in both temporal and spatial dimensions. (2) When the NaCl concentration ranged from 3.0 to 8.0 g/L, an increase in salt concentration facilitated microbial growth. However, the microbial growth was inhibited at the NaCl concentration of 18.0 g/L. (3) The flow field in porous media was significantly affected by the microbial growth at the different NaCl concentration. However, the overall nutrient concentration field in porous media was not relative to the microbial growth. (4) The occurring moments of bio-clogging are 68.0, 66.0, 56.0, and 91.0 h at the NaCl concentrations of 3.0, 6.0, 8.0, and 18.0 g/L.

Predicting the geometrical evolution of the pore space in geological formations due to fluid–solid interactions has applications in reservoir engineering, oil recovery, and geological storage of carbon dioxide. However, modeling frameworks that combine fluid flow with physical and chemical processes at a rock’s pore scale are scarce. Here, we report a method for modeling a rock’s pore space as a network of connected capillaries and to simulate the capillary diameter modifications caused by reactive flow processes. Specifically, we model mineral erosion, deposition, dissolution, and precipitation processes by solving the transport equations iteratively, computing diameter changes within each capillary of the network simultaneously. Our automated modeling framework enables simulations with digital rock samples as large as (1.125 mm)(^3) having 125(times) 10(^6) voxels, within seconds of CPU time per iteration. As an application of the computational method, we have simulated brine injection and calcium carbonate precipitation in sandstone. For quantitatively comparing simulation results obtained with models predicting either a constant or a flow rate-dependent precipitation, we track the time-dependent capillary diameter distribution as well as the permeability of the connected pore space. For validation and reuse, we have made the automated simulation workflow, the reactive flow model library, and the digital rock samples available in public repositories.