Pore Space Geometry Research Articles

Evaluation of the effectiveness of the method of studying water saturation for solving urgent decarbonization problems

At present, the impact of the decarbonization trend on the oil and gas industry is becoming more and more tangible. One of the promising approaches to carbon dioxide utilization is the technology of its injection into the productive formation for enhanced oil recovery. The projects realized in the world have shown the efficiency of liquefied carbon dioxide application as a solvent for wells, which leads to increase of hydrocarbon flow rate due to various mechanisms. CO2 injection has been proven to reduce the interfacial tension at the hydrocarbon-water interface and improve water wettability of the rock, converting it from a film state to a droplet state. However, when implementing this approach, it is worth paying attention to the study of formation parameters such as saturation and capillary forces. In this paper, the capillarometry principle is considered to calculate the water saturation parameter of a complex reservoir type with low filtration properties. The study is to evaluate an effective approach to study the estimated residual water saturation under complex pore space geometry and possible pore hydrophilicity disturbance. The practical interest of this work is to increase the study of reservoir properties at the pore level for prediction of its physical and chemical properties in the modeling of composite dynamic model for solving topical issues in reservoir development.

Colloid Transport and Retention in Constricted Tube Pore Spaces With Diverse Geometries and Orientations

AbstractColloidal contaminants infiltrate and can be attached onto grain surfaces of soils and aquifers, where they may persist. In this study, Lagrangian particle tracking is used to investigate particle trajectories and attachment in pore and fracture spaces modeled as three‐dimensional constricted tubes with diverse geometries and orientations relative to gravity. A comprehensive force balance arising from hydrodynamic drag and lift, gravitational settling, Brownian motion, and attractive DLVO interactions is simulated. Results show that the collection efficiency η is primarily governed by the dimensionless settling number 𝑆, representing the relative dominance of gravitational over hydrodynamic forces experienced by the particles. High‐𝑆 scenarios have larger η and are more sensitive to pore orientation, while low‐𝑆 scenarios are more sensitive to pore geometry. For all scenarios but especially low‐S scenarios, the majority of colloid attachment occurs near pore extremities, where fluid velocities are low, such that mechanical remobilization of particles attached is improbable. In low‐𝑆 scenarios, particles may spread and become immobilized at greater distances from the contamination source owing to lower η, are harder to mechanically remobilize as they attach more disproportionately at pore extremities, and have trajectories more sensitive to minor forces, rendering their environmental fates complex. Characterizing the collection efficiency and deposition morphology for various pore space geometries and orientations is crucial in understanding particle fate and developing continuum‐scale models of colloid transport in real soils, where pore spaces are heterogeneous and advection paths are tortuous.

Influence of the structure of the pore space of rocks on the wettability and adsorbed fluids of reservoir rocks

Theoretical and experimental studies of the effect of the geometry of the pore space on the wettability of collectors have been carried out. The special structure of the pore space of the Talinskaya area of the Krasnoleninsky deposit, due to the heterogeneity of the composition of rocks, leads to heterogeneity in the size and geometry of filtration channels and affects wettability. The specificity of the adsorption of polar components of oil causes changes in wettability and affects the amount of adsorbed oil. Keywords: pore space geometry; adsorbed fluids; wettability.

Spatial Characterization of Wetting in Porous Media Using Local Lattice-Boltzmann Simulations

Wettability is one of the critical parameters affecting multiphase flow in porous media. The wettability is determined by the affinity of fluids to the rock surface, which varies due to factors such as mineral heterogeneity, roughness, ageing, and pore-space geometry. It is well known that wettability varies spatially in natural rocks, and it is still generally considered a constant parameter in pore-scale simulation studies. The accuracy of pore-scale simulation of multiphase flow in porous media is undermined by such inadequate wettability models. The advent of in situ visualization techniques, e.g. X-ray imaging and microtomography, enables us to characterize the spatial distribution of wetting more accurately. There are several approaches for such characterization. Most include the construction of a meshed surface of the interface surfaces in a segmented X-ray image and are known to have significant errors arising from insufficient resolution and surface-smoothing algorithms. This work presents a novel approach for spatial determination of wetting properties using local lattice-Boltzmann simulations. The scheme is computationally efficient as the segmented X-ray image is divided into subdomains before conducting the lattice-Boltzmann simulations, enabling fast simulations. To test the proposed method, it was applied to two synthetic cases with known wettability and three datasets of imaged fluid distributions. The wettability map was obtained for all samples using local lattice-Boltzmann calculations on trapped ganglia and optimization on surface affinity parameters. The results were quantitatively compared with a previously developed geometrical contact angle determination method. The two synthetic cases were used to validate the results of the developed workflow, as well as to compare the wettability results with the geometrical analysis method. It is shown that the developed workflow accurately characterizes the wetting state in the synthetic porous media with an acceptable uncertainty and is better to capture extreme wetting conditions. For the three datasets of imaged fluid distributions, our results show that the obtained contact angle distributions are consistent with the geometrical method. However, the obtained contact angle distributions tend to have a narrower span and are considered more realistic compared to the geometrical method. Finally, our results show the potential of the proposed scheme to efficiently obtain wettability maps of porous media using X-ray images of multiphase fluid distributions. The developed workflow can help for more accurate characterization of the wettability map in the porous media using limited experimental data, and hence more accurate digital rock analysis of multiphase flow in porous media.

Estimating relative diffusion from 3D micro-CT images using CNNs

In recent years, convolutional neural networks (CNNs) have demonstrated their effectiveness in predicting bulk parameters, such as effective diffusion, directly from pore-space geometries. CNNs offer significant computational advantages over traditional methods, making them particularly appealing. However, the current literature primarily focuses on fully saturated porous media, while the partially saturated case is also of high interest for various applications. Partially saturated conditions present more complex geometries for diffusive transport, making the prediction task more challenging. Traditional CNNs tend to lose robustness and accuracy with lower saturation rates. In this paper, we overcome this limitation by introducing a CNN, which conveniently fuses diffusion prediction and a well-established morphological model that describes phase distributions in partially saturated porous media. We demonstrate the ability of our CNN to perform accurate predictions of relative diffusion directly from full pore-space geometries. Finally, we compare our predictions with well-established relations such as the one by Millington–Quirk.

Physical and Chemical Stability of Nanoparticles in Ferrofluid Before and After Impregnation: Implications for Magnetic Pore Fabric Studies

AbstractMagnetic pore fabrics (MPFs) are a promising approach to explore the 3D pore space of rocks. There exist empirical relationships between the orientation, degree, and shape of MPF with pore space geometry and permeability anisotropy. Nevertheless, the precise nature of these relationships remains elusive. A common assumption in MPF studies is that ferrofluid uniformly fills the pore space, establishing a constant nanoparticle density and magnetic properties in space and time. However, this assumption is challenged by observations of particle sedimentation and time‐dependent magnetic properties, which render the quantitative interpretation of MPFs challenging. This study explores the physical and chemical stability of ferrofluid, mobility of particles after impregnation, and the magnetic properties of impregnated rocks over time. Water‐based ferrofluids are physically more stable than oil‐based ferrofluids, though with higher magnetic variability. In impregnated rock, magnetic nanoparticles display a certain degree of mobility, resulting in changes in MPF degree and shape. When ferrofluid is mixed with epoxy, particles are less mobile, though some changes were observed both during polymerization and aging. The susceptibility of ferrofluid‐epoxy mixtures is lower than for ferrofluid and carrier liquid at the same concentration. Interestingly, the susceptibility of impregnated rock increases over time, regardless of the ferrofluid used for the impregnation. Understanding and controlling these processes will enhance the reliability of MPF interpretations and increase the applicability of the method.

A seismic-reflection-based approach for determining the hydraulic permeability of rocks in a subsurface region

Abstract. The hydraulic permeability of rocks characterizes fluid mobility in the pores of underground reservoirs. Accurate information on this quantity is important for various geotechnology applications with urgent social, environmental and political significance (e.g., the exploration, optimization and monitoring of underground disposals of gases, fluids and radioactive wastes; safe underground engineering operations; and the exploration and development of geothermal and hydrocarbon energy). Because of the high resolution and large penetration range of seismic waves as well as the ability of seismic exploration to operate from the Earth's surface, a seismic-reflection-based method for characterizing underground permeability is very important. The elastic properties of rocks of a given lithology are strongly influenced by the compliant pore space. However, the hydraulic permeability of such rocks can be controlled by compliant pores, stiff pores or both types of pores. Using stress-dependent rock-physics experiments, we derive models of elastic properties (e.g., seismic velocities) as functions of compliant and stiff pore-space geometries. Simultaneously analyzing the stress-dependent behavior of the porosity and of the permeability, we identify which part of the pore space controls the permeability – compliant and/or stiff pore space – and derive a corresponding model. These models are used to directly characterize stiff and compliant pore spaces from seismic velocities and to calculate the underground permeability.

Microstructural analysis of slag properties associated with calcite precipitation due to passive CO2 mineralization

CO2 mineralization in slag has gained significant attention since it occurs with minimal human intervention and energy input. While the amount of theoretical CO2 that can be captured within slag has been quantified based on slag composition in several studies, the microstructural and mineralogical effects of slag on its ability to capture CO2 have not been fully addressed. In this work, the CO2 uptake within legacy slag samples is analyzed through microstructural characterization. Slag samples were collected from the former Ravenscraig steelmaking site in Lanarkshire, Scotland. The collected samples were studied using X-ray Computed Tomography (XCT) to understand the distribution and geometry of pore space, as well as with scanning electron microscopy (SEM) coupled with energy-dispersive X-ray spectroscopy (EDS) to visualize the distribution of elements within the studied samples. Electron backscatter diffraction (EBSD) was used to study the minerals distribution. The samples were also characterized through X-ray diffraction (XRD) and X-ray fluorescence (XRF), and the amount of captured CO2 was quantified using thermogravimetric analysis (TGA). Our results demonstrate that CO2 uptake occurs to the extent of ∼9–30 g CO2/ kg slag. The studied samples are porous in nature, with pore space occupying up to ∼30% of their volumes, and they are dominated by åkermanite-gehlenite minerals which interact with the atmospheric CO2 slowly at ambient conditions. EDS and EBSD results illustrate that the precipitated carbonate in slag is calcite, and that the precipitation of calcite is accompanied by the formation of a Si-O-rich layer. The provided analysis concludes that the porous microstructure as well as the minerals distribution in slag should be considered in forecasting and designing large-scale solutions for passive CO2 mineralization in slag.

Theoretically derived pore geometry in carbonates using the extended biot theory

Carbonates typically display a large scatter in velocity-porosity cross plots that is caused by the difference in stiffness that the highly variable pore geometry produces in carbonate rocks at a given porosity. In recent years, digital image analysis (DIA) made it possible to capture objective, quantifiable parameters of the pore geometry to explain the scatter in carbonate velocity-porosity cross plots. The geometrical parameters most influential for acoustic velocity are Perimeter over Area (measuring the complexity of the pore space) and Dominant Size (measuring pore sizes as equivalent diameter). Most theoretical models of acoustic wave propagation in porous media, however, do not incorporate those geometrical characteristics of the natural pore spaces? In contrast, the frame flexibility and coupling factors (fk and γk) embedded in the Extended Biot Theory capture the geometrical characteristics using poroelastic data. Here, these theoretically derived parameters are tested and validated with experimental data. In 95 limestone samples, Extended Biot Theory parameters fk and γk can be derived reliably from measured acoustic data. Both parameters correlate well to Perimeter over Area (r = 0.702, p < 0.0001), and Dominant Size (r = 0.792, p < 0.0001) derived using digital image analysis of thin section photographs. These correlations confirm the existence and the strength of the relationship between theoretical parameters from the Extended Biot Theory and quantitative pore geometry parameters. Thus, this study illustrates that: (1) estimating porosity from acoustic data can be substantially improved by incorporating information on quantitative pore space geometry; and (2) in cases where good estimates of porosity are available (e.g. from well-log data or seismic with extensive well control) quantitative pore geometric characteristics can be estimated directly from acoustic data. These quantitative pore geometry characteristics can be used to estimate permeability indirectly from acoustic data. For example, permeability estimates based on a Kozeny-Carman type function incorporating geometrical information derived from the topological Extended Biot Theory parameters fk and γk, yield a statistically significant, high Pearson correlation coefficient of r = 0.713 (p < 0.0001).

Clogging and permeability reduction dynamics in porous media: A numerical simulation study

The dynamics of fine particle entrainment, transport, and deposition within pore systems, and in particular, the capacity for mobile fines to impair permeability within porous media is critical in many industrial applications. Considerable effort has been expended over the past several decades to identify and parametrize the governing factors that control permeability reduction, with studies employing a combination of physical experimentation and numerical simulation toward this aim. The objective of this work was to numerically investigate the impact of pore space geometry and fine sizes on the clogging dynamics of porous media. The clogging dynamics were characterized by the length of clogging zone (LCZ), the critical throat size of clogging (CTZC), the clogged fraction of throats (CFT), and the permeability reduction (PR). We implemented a computational fluid dynamics-discrete element method (CFD-DEM) numerical framework with a four-way coupling scheme to obtain insights into throat clogging and permeability reduction within heterogeneous porous media utilizing four different monodisperse suspensions. Geometries of porous media were extracted from 3D images of sand packs obtained using micro-computed tomography. The geometries had porosity values ranging from 0.35 to 0.57 and initial permeabilities between 1.35 and 26.32 μm2. CFD-DEM simulations were performed on each geometry four times, varying the injected fine particle size from 5 to 15 μm diameters. Findings indicate that pore systems with tortuosity <1.28 and angles of solid grains orientation larger than 46o tend to have shorter length clogging zones and greater permeability reduction. Furthermore, although systems with porosities higher than 0.48 tend to have a relatively high clogged fraction of throats, they undergo lower permeability reduction. It was also observed that within the studied pore systems, injection of fine particles larger than 7 μm into systems with aspect ratios higher than 2.5, results in a lower fraction of clogged throats. Additionally, for the studied pore networks,< 7 μm fine particles injected into porous media with a large pore body (> 66 μm) and throat (> 22 μm) radii tended to result in larger clogging zones and a greater critical throat size of clogging.

Influence of hydraulic and geometric conditions on the early stages of the filtration process in idealised granular soils comprising spherical particles

AbstractThe early stages of particle‐scale filtration in idealised granular soils were investigated using coupled Computational Fluid Dynamics and Discrete Element Method. Filtration simulations were conducted for assemblies of finer base particles underlying coarser filter particles with upward seepage flow perpendicular to the base‐filter interface. A wide range of size ratios that covered the complete spectrum of filtration behaviours were considered, along with different ramping accelerations for seepage flow to investigate the combined influence of geometric characteristics and hydraulic loading. The influence of flow conditions and size ratios on the time‐dependent infiltration depth of transported base particles was observed to be universal with respect to a newly proposed dimensionless time parameter. The susceptibility to filtration during the early stages was quantitatively investigated with the number of infiltrating base particles and the filling degree of pores within the filter. The susceptibility during the early stages was shown to have a strong correlation with pore space geometry at the filter boundary, which was also reflected by the maximum pore filling degree of the filter. While filter susceptibility has been conventionally evaluated by the size ratio of the assembly, the consideration of the pore space geometry at the filter boundary provided an alternate approach of evaluating local filter susceptibility.

Rate-Controlled Mercury Injection Experiments to Characterize Pore Space Geometry of Berea Sandstone

Interpretation of the relationship between heterogeneity and the flow in porous media is very important in increasing the recovery factor for an oil or gas reservoir. Capillarity for instance, controls fluids static distribution in a reservoir prior to production and remaining hydrocarbons after production commences. Therefore, capillary pressure data are used by petroleum engineers, geologists, and petrophysicists to evaluate production characteristics of petroleum accumulations. Conventional pressure-controlled mercury porosimetry produces an overall capillary pressure curve and pore throat size distribution data that provide little information about the porous medium structure and pore geometry. The present study provides information on three capillary pressure curves obtained from rate-controlled mercury injection porosimetry; one describes the larger pore spaces or pore bodies of a rock, another describes the smaller pores or pore throats that connect the larger pores, and a final curve which corresponds to the overall capillary pressure curve obtained from the conventional pressure-controlled mercury injection. An experimental constant-rate mercury injection apparatus was constructed that consists of a piston displacement pump, a computer controlled stepper motor drive and a core sample cell designed to minimize dead volume. The apparatus was placed in a glass chamber and subjected to an air bath to maintain a constant temperature of 27o C throughout the experiments. Then constant rate mercury injection experiments were performed on three Berea Sandstone core plugs. Results show that volume-controlled or rate-controlled porosimetry provides considerably more detailed data and information on heterogeneity and the statistical nature of pore space structure than the conventional pressure-controlled porosimetry as pressure fluctuations with time reveal menisci locations in pore bodies and pore throats. Moreover, pore size distributions based on volume-accessed pores and pore radii were obtained from the pressure versus saturation relationship.

Colloidal transport in anisotropic porous media: Kinetic equation and its upscaling

We discuss three-dimensional suspension-colloidal-nano transport in anisotropic porous media. The Boltzmann kinetic equation captures stochastic particle velocity distribution on the micro-scale due to the stochastic nature of the pore-space geometry. We express the anisotropy by tensorial micro-scale transport coefficients and a direction-dependent velocity distribution. The Bhatnagar–Gross–Krook (BGK) form of Boltzmann’s equation for particle transport with a particle capture rate proportional to the particle velocity is formulated. We introduce an equivalent sink term into the kinetic equation instead of non-zero initial data, yielding an evolution system not requiring Cauchy’s data. Exact probabilistic homogenisation is performed by solution of a functional operator equation in Hilbert space of Fourier images. The homogenised macro-scale model has the form of a three-dimensional advective–diffusive–reactive equation, where the particle flow in all directions is slower than the carrier fluid speed. This delay is the collective result of particle flow and capture and is explained by preferential capture of faster particles. Closed expressions of upscaled transport coefficients as a function of microscale parameters are explicit. For the particular case of quasi-one-dimensional flow, where the average transverse flux is zero, we derive the upscaled equation and investigate the continuous and pulse colloidal injections.

A novel hybrid thermodynamic model for pore size distribution characterisation for shale

Scholars often see the gas adsorption technique as a straight-to-interpret technique and adopt the pore size distribution (PSD) given by the gas adsorption technique directly to interpret pore-structure-related issues. The oversimplification of interpreting shale PSD based on monogeometric thermodynamic models leads to apparent bias to the realistic pore network. This work aims at establishing a novel thermodynamic model for shale PSD interpretation. We simplified the pore space into two geometric types—cylinder-shaped and slit-shaped. Firstly, Low-temperature Nitrogen Adsorption data were analyzed utilizing two monogeometric models (cylindrical and slit) to generate PSDcyl. and PSDslit; Secondly, pore geometric segmentation was carried out using Watershed by flooding on typical SEM images to obtain the ratio of slit-shaped ∅s and cylinder-shaped pores ∅c. Combining the results of the two, we proposed a novel hybrid model. We performed pyrolysis, XRD, FE-SEM observation, quantitative comparison with the results obtained by the DFT model, and fractal analysis to discuss the validity of the obtained PSDHybrid. The results showed that: the hybrid model proposed in this work could better reflect the real geometry of pore space and provide a more realistic PSD; compared with thermodynamic monogeometric models, PSD obtained from the hybrid model are closer to that from the DFT model, with an improvement in the deviation from the DFT model from 5.06% to 68.88%. The proposed hybrid model has essential application prospects for better interpretation of shale pore space. It is also worth noting that we suggest applying the proposed hybrid model for PSD analysis in the range of 5–100 nm.

Weighted geometric mean model for determining thermal conductivity of reservoir rocks: Current problems with applicability and the model modification

Thermal conductivity is one of the fundamental physical parameters of rocks used in all fields of Earth science related to heat transfer in formations. Theoretical modeling of thermal conductivity plays a significant role when it is impossible to carry out measurements on rock samples or when it is necessary to account for variations in bulk thermal conductivity caused by natural or technological factors. Among many theoretical models for determining the thermal conductivity of reservoir rocks, the weighted geometric mean model or so-called Lichtenecker model is the most popular. We demonstrate that the model systematically underestimates the predicted thermal conductivity relative to the experimental results obtained from the thermal conductivity measurements on 20 collections of 1765 samples represented by sandstones, limestone dolomites, limestones, siltstones, and argillites in various saturation states. The underestimation value depends on lithology, porosity, and pore fluid and can reach 53% for high porous (27.6% of porosity) gas-saturated rocks: the average value of the underestimation is 20.5% for dried samples, 14.4% for kerosene-saturated, and 8.6% for water-saturated collections. It means that the applicability of the weighted geometric mean model for determining the thermal conductivity of reservoir rocks creates a serious risk of essential errors in the predicted data that requires revision and improvement of the theoretical model. It is shown that using the correction factor in the weighted geometric mean model resolves the problem leading to an average absolute relative deviation of less than 0.45% for dried and kerosene-saturated samples and less than 0.3% for water-saturated samples. We presented the technique for estimations of the factor for different rock types with corresponding results, and studied its relation with the pore space geometry of carbonate rocks using Effective Medium Theory. Interrelations were studied between the correction factor, rock types, pore fluid, and pore space characteristic - aspect ratio. The suggested improvement technique can be used in numerical calculations after modifying the corresponding option of thermo-hydrodynamic simulators or in theoretical calculations to decrease the uncertainty in bulk thermal conductivity and increase the reliability of calculation results.

A Probabilistic Formulation of the Diffusion Coefficient in Porous Media as Function of Porosity

We investigate the upscaling of diffusive transport parameters using a stochastic framework. At sub-REV (representative elementary volume) scale, the complexity of the pore space geometry leads to a significant scatter of the observed diffusive transport. We study a large set of volumes reconstructed from focused ion beam-scanning electron microscopy data. Each individual volume provides us sub-REV measurements on porosity and the so-called transport-ability, being a dimensionless parameter representing the ratio of diffusive flux through the porous volume to that through an empty volume. The detected scatter of the transport-ability is mathematically characterized through a probability distribution function (PDF) with a mean and variance as function of porosity, which includes implicitly the effect of pore structure differences among sub-REV volumes. We then investigate domain size effects and predict when REV scale is reached. While the scatter in porosity observations decreases linearly with increasing sample size as expected, the observed scatter in transport-ability does not converge to zero. Our results confirm that differences in pore structure impact transport parameters at all scales. Consequently, the use of PDFs to describe the relationship of effective transport coefficients to porosity is advantageous to deterministic semiempirical functions. We discuss the consequences and advocate the use of PDFs for effective parameters in both continuum equations and data interpretation of experimental or computational work. The presented statistics-based upscaling technique of sub-REV microscopy data provides a new tool in understanding, describing and predicting macroscopic transport behavior of microporous media.

Pore-scale simulation of fine particles migration in porous media using coupled CFD-DEM

Transport of fine particles in porous media has attracted a considerable interest over the past several decades given its importance to many industrial and natural processes. In order to control fine particles transport in porous media, the behavior itself needs to be predicted and analyzed. Which could be quite costly to do in a laboratory experiment, hence the modelling of such a behavior would be an optimum way to study. In this work, we present a coupled computational fluid dynamics-discrete element method (CFD-DEM) based modelling framework that is capable of simulating the migration of a large number of fine particles, on the colloids size through physically realistic porous media. The model was parallelized using Message Passing Interface (MPI) protocol to resolve particle-particle and particle-grain interactions for a large number of fine particles. The model's geometry was generated from computed tomography images of sand packs, the CFD solver were set to solve the fluid flow while the DEM were to solve the particles physical interactions. Immersed Boundary Method (IBM) were used to couple the two solvers in a resolved manner. The model's setup was first validated to capture single phase fluid flow behavior and then the coupling of the fluid flow and particle interaction was compared to experimental results. In order to validate the coupled CFD-DEM model, velocity profiles of fine particles, their diffusion in the pore space and the percentage of fine particles retained in the pore space obtained from micromodel experiment were compared to values obtained from simulations using the same pore space geometry and initial experimental conditions. The presented framework was shown to capture the bulk dynamics of fine particulate transport and deposition within geologically realistic and complex flow domains while minimizing execution time.The model was used to study the impact of flow velocity and size of fines on permeability reduction of porous media due to migration of fine particles. Simulations indicate that permeability reduction due to fine migration in porous media is directly proportional to flow velocity. The time required to develop bridging and subsequent clogging of the pore space that lead to permeability reduction decreases as the flow velocity increases. The size of the fine particles has a significant impact on permeability reduction where the reduction in permeability increases as the size of particles increases. A faster and larger reduction in permeability was observed when a suspension of polydisperse particles was injected as opposed to a monodisperse suspension.

Main modes of microfilament particles deformation in rough channels

The fate and transport of microfilaments in complex structured porous systems are largely affected by the geometry of the irregular pore space in these media. Local features of fluid flow, including local flow instabilities, vorticities, stagnant zones, and reverse flows, which result from the spatially varying pore throat size and altering shear stresses along the channel due to the presence of rough walls, can cause various modes of deformation of filaments and them being carried in reverse direction of the general fluid flow. Furthermore, the buildup of microfilaments along the channel can clog the pore space and rearrange the flow in the channel. In this study, we focus on investigating the role of channel wall roughness on the motion and deformation of five deformable filaments flowing in a channel filled with fluid. A bead-spring model is used for the filament model. At low Reynolds numbers, roughness simply increases the length of the path line along which the filament is being transported. Moreover, at higher Reynolds numbers, the filament closer to the walls can get stuck in the dead flow zones within the rough geometry peaks. The filaments closer to the centerline of the channel undergo less deformation compared to those located closer to the walls. A larger Reynolds number or a more rough geometry of the walls can result in a more wiggly form of the filament. Intermediate roughness and a medium Reynolds number result in more of a hairpin-like filament shape.

Retention of sulfidated nZVI (S-nZVI) in porous media visualized by X-ray μ-CT – the relevance of pore space geometry

Relating S-nZVI nanoparticle retention, spatially resolved via X-ray μ-CT, to different retention mechanisms, such as straining, ripening and sorption deposition, via 3D pore local thickness (LT).

Machine learning applied to pore-space geometry in sandstones: a tool for evaluating grain-scale similarity?

The ability to identify similar sandstones to a given sample is important where the provenance of the sample is unknown or the quarry of origin is no longer in operation. In the case of building stones from heritage buildings in protected areas, it may be mandatory. Here, a proof of concept for an automated similarity measure is presented by means of a convolutional autoencoder that is able to extract features from a sample thin section and use these features to identify the most similar sample in an existing image library. The approach considers only the shape of the pore space between grains, as, if the pore space alone contains enough information to distinguish between samples, the required image pre-processing and training of a model is greatly simplified. The trained model is able to predict correctly the progenitor quarry of a thin section, from an eight-class dataset of Scottish sandstones, with an accuracy of 47.9%. This prototype, although insufficient for commercial purposes, forms a benchmark for future models against which improvements can be assessed and some of which are suggested. Thematic collection: This article is part of the Digitization and Digitalization in engineering geology and hydrogeology collection available at: https://www.lyellcollection.org/cc/digitization-and-digitalization-in-engineering-geology-and-hydrogeology