Porous Media Characterization Using Minkowski Functionals: Theories, Applications and Future Directions

Quantifying colors at micrometer scale by colorimetric microscopy (C-Microscopy) approach

The color is the primal property of the objects around us and is direct manifestation of light-matter interactions. The color information is used in many different fields of science, technology and industry to investigate material properties or for identification of concentrations of substances. Usually the color information is used as a global parameter in a macro scale. To quantitatively measure color information in micro scale one needs to use dedicated microscope spectrophotometers or specialized micro-reflectance setups. Here, the Colorimetric Microscopy (C-Microscopy) approach based on digital optical microscopy and a free software is presented. The C-Microscopy approach uses color calibrated image and colorimetric calculations to obtain physically meaningful quantities i.e., dominant wavelength and excitation purity maps at micro level scale. This allows for the discovery of the local color details of samples surfaces. Later, to fully characterize the optical properties, the hyperspectral reflectance data at micro scale (reflectance as a function of wavelength for a each point) are colorimetrically recovered. The C-Microscopy approach was successfully applied to various types of samples i.e., two metamorphic rocks unakite and lapis lazuli, which are mixtures of different minerals; and to the surface of gold 99.999 % pellet, which exhibits different types of surface features. The C-Microscopy approach could be used to quantify the local optical properties changes of various materials at microscale in an accessible way. The approach is freely available as a set of python jupyter notebooks.

Modeling flow of Carreau fluids in porous media

Carreau fluids occur routinely in porous medium systems for a range of applications, and the dependence of the viscosity for such fluids on the rate of strain tensor poses challenges to modeling at an averaged macroscale. Traditional approaches for macroscale modeling such flows have relied upon experimental observations of flows for generalized Newtonian fluids (GNFs) and a phenomenological approach referred to herein as the shift factor. A recently developed approach based upon averaging conservation and thermodynamic equations from the microscale for Cross model GNFs is extended to the case of Carreau fluids and shown to predict the flow through both isotropic and anisotropic media accurately without the need for GNF-flow experiments. The model is formulated in terms of rheological properties, a standard Newtonian resistance tensor, and a length-scale tensor, which does require estimation. An approach based upon measures of the morphology and topology of the pore space is developed to approximate this length-scale tensor. Thus, this work provides the missing components needed to predict Carreau GNF macroscale flow with only rheological information for the fluid and analysis of the pore morphology and topology independent of any fluid flow experiments. Accuracy of predictions based upon this approach is quantified, and extension to other GNFs is straightforward.

Three-dimensional reconstruction of granular porous media based on deep generative models

Reconstruction of microstructure in granular porous media, which can be viewed as granular assemblies, is crucial for studying their characteristics and physical properties in various fields concerned with the behavior of such media, including petroleum geology and computational materials science. In spite of the fact that many existing studies have investigated grain reconstruction, most of them treat grains as simplified individuals for discrete reconstruction, which cannot replicate the complex geometrical shapes and natural interactions between grains. In this work, a hybrid generative model based on a deep-learning algorithm is proposed for high-quality three-dimensional (3D) microstructure reconstruction of granular porous media from a single two-dimensional (2D) slice image. The method extracts 2D prior information from the given image and generates the grain set as a whole. Both a self-attention module and effective pattern loss are introduced in a bid to enhance the reconstruction ability of the model. Samples with grains of varied geometrical shapes are utilized for the validation of our method, and experimental results demonstrate that our proposed approach can accurately reproduce the complex morphology and spatial distribution of grains without any artificiality. Furthermore, once the model training is complete, rapid end-to-end generation of diverse 3D realizations from a single 2D image can be achieved.

A data-driven framework for permeability prediction of natural porous rocks via microstructural characterization and pore-scale simulation

Understanding the microstructure-property relationships of porous media is of great practical significance, based on which macroscopic physical properties can be directly derived from measurable microstructural informatics. However, establishing reliable microstructure-property mappings in an explicit manner is difficult, due to the intricacy, stochasticity, and heterogeneity of porous microstructures. In this paper, a data-driven computational framework is presented to investigate the inherent microstructure-permeability linkage for natural porous rocks, where multiple techniques are integrated together, including microscopy imaging, stochastic reconstruction, microstructural characterization, pore-scale simulation, feature selection, and data-driven modeling. A large number of 3D digital rocks with a wide porosity range are acquired from microscopy imaging and stochastic reconstruction techniques. A broad variety of morphological descriptors are used to quantitatively characterize pore microstructures from different perspectives, and they compose the raw feature pool for feature selection. High-fidelity lattice Boltzmann simulations are conducted to resolve fluid flow passing through porous media, from which reliable permeability references are obtained. The optimal feature set that best represents permeability is identified through a performance-oriented feature selection process, upon which a cost-effective surrogate model is rapidly fitted to approximate the microstructure-permeability mapping via data-driven modeling. This surrogate model exhibits great advantages over empirical/analytical formulas in terms of prediction accuracy and generalization capacity, which can predict reliable permeability values spanning four orders of magnitude. Besides, feature selection also greatly enhances the interpretability of the data-driven prediction model, from which new insights into the mechanism of how microstructural characteristics determine intrinsic permeability are obtained.

Multiscale modeling of solute diffusion in triblock copolymer membranes

We develop a multiscale simulation model for diffusion of solutes through porous triblock copolymer membranes. The approach combines two techniques: self-consistent field theory (SCFT) to predict the structure of the self-assembled, solvated membrane and on-lattice kinetic Monte Carlo (kMC) simulations to model diffusion of solutes. Solvation is simulated in SCFT by constraining the glassy membrane matrix while relaxing the brush-like membrane pore coating against the solvent. The kMC simulations capture the resulting solute spatial distribution and concentration-dependent local diffusivity in the polymer-coated pores; we parameterize the latter using particle-based simulations. We apply our approach to simulate solute diffusion through nonequilibrium morphologies of a model triblock copolymer, and we correlate diffusivity with structural descriptors of the morphologies. We also compare the model's predictions to alternative approaches based on simple lattice random walks and find our multiscale model to be more robust and systematic to parameterize. Our multiscale modeling approach is general and can be readily extended in the future to other chemistries, morphologies, and models for the local solute diffusivity and interactions with the membrane.

Characterization of Two-Phase Flow from Pore-Scale Imaging Using Fractal Geometry under Water-Wet and Mixed-Wet Conditions

High resolution micro-computed tomography images for multiphase flow provide us an effective tool to understand the mechanism of fluid flow in porous media, which is not only fundamental to the understanding of macroscopic measurements but also for providing benchmark datasets to validate pore-scale modeling. In this study, we start from two datasets of pore scale imaging of two-phase flow obtained experimentally under in situ imaging conditions at different water fractional flows under water-wet and mixed-wet conditions. Then, fractal dimension, lacunarity and succolarity are used to quantify the complexity, clustering and flow capacity of water and oil phases. The results show that with the wettability of rock surface altered from water-wet to mixed-wet, the fractal dimension for the water phase increases while for the oil phase, it decreases obviously at low water saturation. Lacunarity largely depends on the degree of wettability alteration. The more uniform wetting surfaces are distributed, the more homogeneous the fluid configuration is, which indicates smaller values for lacunarity. Moreover, succolarity is shown to well characterize the wettability effect on flow capacity. The succolarity of the oil phase in the water-wet case is larger than that in the mixed-wet case while for the water phase, the succolarity value in the water-wet is small compared with that in the mixed-wet, which show a similar trend with relative permeability curves for water-wet and mixed-wet. Our study provides a perspective into the influence that phase geometry has on relative permeability under controlled wettability and the resulting phase fractal changes under different saturations that occur during multiphase flow, which allows a means to understand phase geometric changes that occur during fluid flow.

Molecular Methods for Assessing the Morphology, Topology, and Performance of Polyamide Membranes

The molecular-scale morphology and topology of polyamide composite membranes determine the performance characteristics of these materials. However, molecular-scale simulations are computationally expensive and morphological and topological characterization of molecular structures are not well developed. Molecular dynamics simulation and analysis methods for the polymerization, hydration, and quantification of polyamide membrane structures were developed and compared to elucidate efficient approaches for producing and analyzing the polyamide structure. Polymerization simulations that omitted the reaction-phase solvent did not change the observed hydration, pore-size distribution, or water permeability, while improving the simulation efficiency. Pre-insertion of water into the aggregate pores (radius ≈ 4 Å) of dry domains enabled shorter hydration simulations and improved simulation scaling, without altering pore structure, properties, or performance. Medial axis and Minkowski functional methods were implemented to identify permeation pathways and quantify the polyamide morphology and topology, respectively. Better agreement between simulations and experimentally observed systems was accomplished by increasing the domain size rather than increasing the number of ensemble realizations of smaller systems. The largest domain hydrated was an order of magnitude larger by volume than the largest domain previously reported. This work identifies methods that can enable more efficient and meaningful fundamental modeling of membrane materials.

2D-to-3D image translation of complex nanoporous volumes using generative networks

Image-based characterization offers a powerful approach to studying geological porous media at the nanoscale and images are critical to understanding reactive transport mechanisms in reservoirs relevant to energy and sustainability technologies such as carbon sequestration, subsurface hydrogen storage, and natural gas recovery. Nanoimaging presents a trade off, however, between higher-contrast sample-destructive and lower-contrast sample-preserving imaging modalities. Furthermore, high-contrast imaging modalities often acquire only 2D images, while 3D volumes are needed to characterize fully a source rock sample. In this work, we present deep learning image translation models to predict high-contrast focused ion beam-scanning electron microscopy (FIB-SEM) image volumes from transmission X-ray microscopy (TXM) images when only 2D paired training data is available. We introduce a regularization method for improving 3D volume generation from 2D-to-2D deep learning image models and apply this approach to translate 3D TXM volumes to FIB-SEM fidelity. We then segment a predicted FIB-SEM volume into a flow simulation domain and calculate the sample apparent permeability using a lattice Boltzmann method (LBM) technique. Results show that our image translation approach produces simulation domains suitable for flow visualization and allows for accurate characterization of petrophysical properties from non-destructive imaging data.

Flow estimation solely from image data through persistent homology analysis

Topological data analysis is an emerging concept of data analysis for characterizing shapes. A state-of-the-art tool in topological data analysis is persistent homology, which is expected to summarize quantified topological and geometric features. Although persistent homology is useful for revealing the topological and geometric information, it is difficult to interpret the parameters of persistent homology themselves and difficult to directly relate the parameters to physical properties. In this study, we focus on connectivity and apertures of flow channels detected from persistent homology analysis. We propose a method to estimate permeability in fracture networks from parameters of persistent homology. Synthetic 3D fracture network patterns and their direct flow simulations are used for the validation. The results suggest that the persistent homology can estimate fluid flow in fracture network based on the image data. This method can easily derive the flow phenomena based on the information of the structure.

Capillary equilibrium of bubbles in porous media

In geologic, biologic, and engineering porous media, bubbles (or droplets, ganglia) emerge in the aftermath of flow, phase change, or chemical reactions, where capillary equilibrium of bubbles significantly impacts the hydraulic, transport, and reactive processes. There has previously been great progress in general understanding of capillarity in porous media, but specific investigation into bubbles is lacking. Here, we propose a conceptual model of a bubble's capillary equilibrium associated with free energy inside a porous medium. We quantify the multistability and hysteretic behaviors of a bubble induced by multiple state variables and study the impacts of pore geometry and wettability. Surprisingly, our model provides a compact explanation of counterintuitive observations that bubble populations within porous media can be thermodynamically stable despite their large specific area by analyzing the relationship between free energy and bubble volume. This work provides a perspective for understanding dispersed fluids in porous media that is relevant to CO₂ sequestration, petroleum recovery, and fuel cells, among other applications.

Nanothermodynamic Description and Molecular Simulation of a Single-Phase Fluid in a Slit Pore

We have described for the first time the thermodynamic state of a highly confined single-phase and single-component fluid in a slit pore using Hill's thermodynamics of small systems. Hill's theory has been named nanothermodynamics. We started by constructing an ensemble of slit pores for controlled temperature, volume, surface area, and chemical potential. We have presented the integral and differential properties according to Hill, and used them to define the disjoining pressure on the new basis. We identified all thermodynamic pressures by their mechanical counterparts in a consistent manner, and have given evidence that the identification holds true using molecular simulations. We computed the entropy and energy densities, and found in agreement with the literature, that the structures at the wall are of an energetic, not entropic nature. We have shown that the subdivision potential is unequal to zero for small wall surface areas. We have showed how Hill's method can be used to find new Maxwell relations of a confined fluid, in addition to a scaling relation, which applies when the walls are far enough apart. By this expansion of nanothermodynamics, we have set the stage for further developments of the thermodynamics of confined fluids, a field that is central in nanotechnology.

Evaluation of methods using topology and integral geometry to assess wettability

HYPOTHESIS

The development of high-resolution in situ imaging has allowed contact angles to be measured directly inside porous materials. We evaluate the use of concepts in integral geometry to determine contact angle. Specifically, we test the hypothesis that it is possible to determine an average contact angle from measurements of the Gaussian curvature of the fluid/fluid meniscus using the Gauss-Bonnet theorem.

THEORY AND SIMULATION

We show that it is not possible to unambiguously determine an average contact angle from the Gauss-Bonnet theorem. We instead present an approximate relationship: 2πn(1-cosθ)=4π-∫κ_G12dS₁₂, where n is the number of closed loops of the three-phase contact line where phases 1 and 2 contact the surface, θ is the average contact angle, while κ_G12 is the Gaussian curvature of the fluid meniscus which is integrated over its surface S₁₂. We then use the results of pore-scale lattice Boltzmann simulations to assess the accuracy of this approach to determine a representative contact angle for two-phase flow in porous media.

FINDINGS

We show that in simple cases with a flat solid surface, the approximate expression works well. When applied to simulations on pore space images, the equation provides a robust estimate of contact angle, accurate to within 3°, when averaged over many fluid clusters, although individual values can have significant errors because of the approximations used in the calculation.

Quantitative analysis of phase topology evolution during three-phase displacements in porous media

Multiphase flow in subsurface formations is the essence of aquifer remediation and petroleum recovery processes, where the phase mobilities are greatly influenced by phase topologies. Yet, flow models rarely utilize quantified phase topologies due to the limited availability of such data. Here, we conducted cutting-edge experiments using a micromodel together with a state-of-the-art automated imaging system to capture images with high temporal and areal resolution to characterize the phase topologies for three-phase displacements. The micromodel setup used in this study is a close replica of flow in natural rocks as the resulting three-phase saturation routes agrees well with results of rocks core floods. The injection sequence was repeated with different fluids to compare the effects of fluid properties on phase topologies. The resulting high fidelity images were used to calculate topological parameters such as Euler characteristic, the contact area between phases, flowing sub-phase, wetted rock surface area and saturations. We show that the trend of topological parameters helps to identify the dominant pore scale mechanisms. Furthermore, the developed workflow assists with verification of the microfluidic devices and mechanistic scaling of the microfluidic results to the desired condition.

Fast Fourier transform and support-shift techniques for pore-scale microstructure classification using additive morphological measures

The Minkowski functionals, as the full set of additive morphological measures in three dimensions (3D) consisting of volume, surface area, mean curvature, and total curvature, can be calculated directly by evaluating the local contributions of vertices of a discrete structure. They are sensitive measures of microstructure, and for microstructures generated by a Boolean process, relate to their physical properties. In this work we introduce fast numerical techniques based on the additivity of the Minkowski functionals to derive fields of regional Minkowski measures over large regional support for large 3D data sets as generated, e.g., from x-ray tomography techniques. We demonstrate the application of these 3D feature fields to microstructure classification for a set of heterogeneous microstructures using a multivariate Gaussian mixture model and a thin-bedded sandstone. It is shown that for the case of a spatially heterogeneous Boolean process the internal boundaries of the generating process are recovered with high accuracy, while for the thin-bedded sandstone, compact partitions with clear layering are extracted.

Effect of Cold-Sintering Parameters on Structure, Density, and Topology of Fe-Cu Nanocomposites

The design of advanced nanostructured materials with predetermined physical properties requires knowledge of the relationship between these properties and the internal structure of the material at the nanoscale, as well as the dependence of the internal structure on the production (synthesis) parameters. This work is the first report of computer-aided analysis of high pressure consolidation (cold sintering) of bimetallic nanoparticles of two immiscible (Fe and Cu) metals using the embedded atom method (EAM). A detailed study of the effect of cold sintering parameters on the internal structure and properties of bulk Fe–Cu nanocomposites was conducted within the limitations of the numerical model. The variation of estimated density and bulk porosity as a function of Fe-to-Cu ratio and consolidation pressure was found in good agreement with the experimental data. For the first time, topological analysis using Minkowski functionals was applied to characterize the internal structure of a bimetallic nanocomposite. The dependence of topological invariants on input processing parameters was described for various components and structural phases. The model presented allows formalizing the relationship between the internal structure and properties of the studied nanocomposites. Based on the obtained topological invariants and Hadwiger’s theorem we propose a new tool for computer-aided design of bimetallic Fe–Cu nanocomposites.

Predicting Resistivity and Permeability of Porous Media Using Minkowski Functionals

Permeability and formation factor are important properties of a porous medium that only depend on pore space geometry, and it has been proposed that these transport properties may be predicted in terms of a set of geometric measures known as Minkowski functionals. The well-known Kozeny–Carman and Archie equations depend on porosity and surface area, which are closely related to two of these measures. The possibility of generalizations including the remaining Minkowski functionals is investigated in this paper. To this end, two-dimensional computer-generated pore spaces covering a wide range of Minkowski functional value combinations are generated. In general, due to Hadwiger’s theorem, any correlation based on any additive measurements cannot be expected to have more predictive power than those based on the Minkowski functionals. We conclude that the permeability and formation factor are not uniquely determined by the Minkowski functionals. Good correlations in terms of appropriately evaluated Minkowski functionals, where microporosity and surface roughness are ignored, can, however, be found. For a large class of random systems, these correlations predict permeability and formation factor with an accuracy of 40% and 20%, respectively.

Roots compact the surrounding soil depending on the structures they encounter

Contradictory evidence exists regarding whether and to which extend roots change soil structure in their vicinity. Here we attempt to reconcile disparate views allowing for the two-way interaction between soil structure and root traits, i.e. changes in soil structure due to plants and changes in root growth due to soil structure. Porosity gradients extending from the root/biopore surface into the bulk soil were investigated with X-ray µCT for undisturbed soil samples from a field chronosequence as well as for a laboratory experiment with Zea mays growing into three different bulk densities. An image analysis protocol was developed, which enabled a fast analysis of the large sample pool (n > 300) at a resolution of 19 µm. Lab experiment showed that growing roots only compact the surrounding soil if macroporosity is low and dominated by isolated pores. When roots can grow into a highly connected macropore system showing high connectivity the rhizosphere is more porous compared to the bulk soil. A compaction around roots/biopores in the field chronosequence was only observed in combination with high root/biopore length densities. We conclude that roots compact the rhizosphere only if the initial soil structure does not offer a sufficient volume of well-connected macropores.

Oil mobilization and solubilization in porous media by in situ emulsification

HYPOTHESIS

For a wide range of subsurface engineering processes, such as geological carbon sequestration and enhanced oil recovery, it is critical to understand multiphase flow at a fundamental level. To this end, geomaterial microfluidic devices provide visual data that can be quantified to explain the physics of multiphase flow at the length scale of individual pores in realistic rock structures. For surfactant enhanced oil recovery, it is the underlying geometrical states of the capillary trapped oil that dictates the recovery process and the degree to which oil is recovered through either mobilization or solubilization during in situ emulsification.

EXPERIMENTS

A novel geomaterial microfluidic device is fabricated and its integrity is checked using light microscopy and X-ray micro-computed tomography (μ-CT) imaging. Subsequently, alkaline surfactant (AS) flooding of an oil saturated device is studied for enhanced recovery. The recovery process is analyzed by collecting 2D radiographic projections of the device during water flooding and in situ emulsification. 3D μ-CT images are also collected to quantify the geometrical states of the fluids after each flooding sequence.

FINDINGS

Our study reveals the processes of oil cluster mobilization and solubilization in porous media. After water flooding there are numerous oil clusters that are relatively large, extending over multiple pores, forming various loop-like structures. These clusters are mobile under AS flooding accounting for 75% of the recovered oil. The less mobile smaller clusters, isolated to single pores, forming no loop-like structures are immobile. These clusters are solubilized during AS flooding accounting for 25% of the recovered oil. The mobilized clusters coalesce forming an oil bank prior to total solubilization. The remaining oil clusters after AS flooding are highly non-wetting, as indicated by contact angle measurements and would only be recoverable after further solubilization.