Porous Media Research Research Articles

Challenges and opportunities for porous media research to address PFAS groundwater contamination

Challenges and opportunities for porous media research to address PFAS groundwater contamination

A Novel Image Processing Approach for Colloid Detection in Saturated Porous Media.

Over recent decades, natural and artificial colloids, as well as nanoparticles, have been increasingly used in various applications. Consequently, with this rising consumption, surface and subsurface environments are more exposed to these particles. The presence of these particles and the colloid-facilitated transport of microorganisms, the interactions between dissolved contaminants and mobile colloids in porous media, and the fate and transport of colloids through groundwater-one of the primary sources of water supply for human societies-have attracted extensive research. This study investigates the performance of several image processing methods in the field of colloid detection, which is a prerequisite for the subsequent steps in porous media research. We employed four different categories of image processing approaches on microscopy images-segmentation-based methods, background-detection-based methods, filter-based methods, and morphology-based methods-to conduct the detection process of colloids. Eight methods were applied and subsequently analyzed in terms of their drawbacks and advantages to determine the best ones in this domain. Finally, we proposed an ensemble approach that leverages the strengths of the three best methods using a majority vote to detect colloids more accurately. In experiments, Precision, Recall, F-measure, and TCR criteria were considered as evaluation tools. Experimental results demonstrate the high accuracy of image processing methods in recognizing colloids. Among all these methods, morphology-based methods were the most successful, achieving the best detection performance and improving the limited distinguishing features of small colloids. Moreover, our ensemble approach, achieving perfect scores across all evaluation criteria, highlights its superiority compared with other detection methods.

Prediction of pore-scale clogging using artificial intelligence algorithms

AbstractWe use five established, but conceptually different artificial intelligence algorithms for analysing clogging and quantifying colloid transport at pore scale: artificial neural networks, decision tree, random forest, linear regression, and support vector regression. We test how these algorithm can predict clogging by interpolating physics based simulation data. Our training and test data set is based on results from Lattice Boltzmann simulations reproducing the physics of colloid transport through a typical pore throat present in glass beads or medium sized sand. We perform hyperparameter optimization through cross validation for all algorithms. The tree based methods have the highest Nash–Sutcliffe efficiencies among all tested algorithms with values mostly above 0.9 for the independent test data. The event of clogging can be predicted even with 100% accuracy. Our results indicate a non-linear, rather categorial nature of the (simulation) data. This is in contrast to the typical use of neural network algorithms for simulation data while tree based methods are often applied to observational data. We partly link this to the small size of our dataset. Our application of artificial intelligence in porous media research shows that time-consuming Lattice Boltzmann simulations can be easily supplemented and extended at small computational costs while predictability of clogging and quantitative effects of process specific parameters on colloidal transport are given with high reliability.

A multifunctional mechanical testing stage for micro x-ray computed tomography

An existing open and modular designed micro X-Ray Computed Tomography (μXRCT) system is extended by a test rig in order to combine mechanical and hydro-mechanical experiments with μXRCT characterization. The aim of the system is to cover the complete resolution range of the underlying μXRCT system in combination with a broad load capacity range. A characteristic feature of the developed setup is that it consists mainly of standard components. This makes the shown test rig potentially interesting for other researchers considering extending an existing μXRCT system with an apparatus for mechanical and hydro-mechanical in situ testing. For the load frame, an uniaxial 10 kN universal testing machine with a digital control system was employed, which was extended by two aligned rotational stages. The uniaxial load capacity is ±3.1 kN and can be combined with torque moments of up to ±15 N m both limited by the used rotational stages. The setup is designed in such a way that different x-ray transparent cells (flow cells, oedometer cells, triaxial cells, etc.) can be integrated to generate three-dimensional stress/strain states as required for porous media research. Three applications demonstrate the possible versatile use of the system. As part of these examples, we show how corresponding x-ray transparent cells are designed and implemented. Finally, we discuss the presented approach’s technical advantages and disadvantages and suggest improvements.

Estimating permeability of 3D micro-CT images by physics-informed CNNs based on DNS

In recent years, convolutional neural networks (CNNs) have experienced an increasing interest in their ability to perform a fast approximation of effective hydrodynamic parameters in porous media research and applications. This paper presents a novel methodology for permeability prediction from micro-CT scans of geological rock samples. The training data set for CNNs dedicated to permeability prediction consists of permeability labels that are typically generated by classical lattice Boltzmann methods (LBM) that simulate the flow through the pore space of the segmented image data. We instead perform direct numerical simulation (DNS) by solving the stationary Stokes equation in an efficient and distributed-parallel manner. As such, we circumvent the convergence issues of LBM that frequently are observed on complex pore geometries, and therefore, improve the generality and accuracy of our training data set. Using the DNS-computed permeabilities, a physics-informed CNN (PhyCNN) is trained by additionally providing a tailored characteristic quantity of the pore space. More precisely, by exploiting the connection to flow problems on a graph representation of the pore space, additional information about confined structures is provided to the network in terms of the maximum flow value, which is the key innovative component of our workflow. The robustness of this approach is reflected by very high prediction accuracy, which is observed for a variety of sandstone samples from archetypal rock formations.

Of rats and rocks: using pre-clinical PET imaging facilities in core analysis

Positron emission tomography (PET) is routinely used for medical imaging; a current surge in published geoscientific research utilizing this modality also infer increasing interest for in-situ PET imaging in core analysis. Excellent signal to noise ratio coupled with high temporal and spatial resolution suggest that PET might become the new method-of-choice for core analysis. Obstacles related to production, transfer and handling of radioactive fluids and gases must, however, be dealt with for PET to become a widely used core scale imaging technique. This paper describes an ongoing, true multidisciplinary collaboration, where pre-clinical PET imaging facilities are routinely used in core analysis to investigate dynamic fluid flow at high pressure conditions. We detail challenges and opportunities related to porous media research in established pre-clinical laboratory facilities designed for small-animal imaging, and demonstrate the significant potential of PET imaging in core scale analysis in a context related to long-term porous media carbon storage. Explicit imaging of several fluid phases is possible by PET imaging using a range of readily available radiotracers. Relevant radiotracers to carbon storage in porous media are e.g. the carbon radioisotope 11C and water-soluble tracer 18F. These are both short-lived tracers (20 - 110 min) and must be used in high doses of radiation, which present challenges related to safe transfer and handling. Although there are several obstacles to conduct advanced core analysis in hospital imaging facilities (some of which are detailed in this paper), significant advantages include trained personnel on-site to operate a local cyclotron, procedures in place to ensure safe and efficient transfer of short-lived radiopharmaceuticals from the cyclotron, and advanced image analysis capabilities available. Cyclotrons are widely available worldwide (currently more than 1200 operating cyclotrons), often located in close proximity to medical and pre-clinical imaging facilities and academic institutions. Similar collaborations may therefore also be possible elsewhere, reducing the need for allocated geophysical PET-scanners and lowering the threshold for routinely using PET imaging in core analysis.

Variational geometric approach to the thermodynamics of porous media

AbstractMany applications of porous media research involves high pressures and, correspondingly, exchange of thermal energy between the fluid and the matrix. While the system is relatively well understood for the case of nonmoving porous media, the situation when the elastic matrix can move and deform, is much more complex. In this paper, we derive the equations of motion for the dynamics of deformable porous media, which includes the effects of friction forces, stresses, and heat exchanges between the media, by using the new methodology of variational approach to thermodynamics. This theory extends the recently developed variational derivation of the mechanics of deformable porous media to include thermodynamic processes and can easily include incompressibility constraints. The model for the combined fluid‐matrix system, written in the spatial frame, is developed by introducing mechanical and additional variables describing the thermal energy part of the system, writing the action principle for the system, and using a nonlinear, nonholonomic constraint on the system deduced from the second law of thermodynamics. The resulting equations give us the general version of possible friction forces incorporating thermodynamics, Darcy‐like forces, and friction forces similar to those used in the Navier–Stokes equations. The equations of motion are valid for arbitrary dependence of the kinetic and potential energies on the state variables. The results of our work are relevant for geophysical applications, industrial applications involving high pressures and temperatures, food processing industry, and other situations when both thermodynamical and mechanical considerations are important.

Recent Advances in Multiscale Petrophysics Characterization and Multiphase Flow in Unconventional Reservoirs

Petrophysics in unconventional reservoirs, especially multiscale characterization and multiphase flow, is relevant to multi-disciplinary porous media research (e [...]

Revisiting movable fluid space in tight fine-grained reservoirs: A case study from Shahejie shale in the Bohai Bay Basin, NE China

Assessment of movable fluid is a key topic in production of tight formations. Evaluation of the movable fluid was usually conducted on tight sandstone formations while rarely on black organic-rich shales. To analyze the movable fluid properties in tight black shales, we document the basic geochemical and petrological features of Paleogene Shahejie shale and carry out quantitative analyses of the movable fluid properties. A modified method is proposed to estimate the pore size distribution. Results indicate that Shahejie shale is characterized by a low porosity and under-saturated of movable fluid. Further, porosity and saturation of movable fluid in shales are mainly determined by the heterogeneity of pore structure. Besides, micropores and mesopores with pore radius smaller than 6.69–18.25 nm (the lower limits are calculated for the collected samples based on the low-field Nuclear Magnetic Resonance (LF-NMR) transverse relaxation time ( T 2 ) spectra, i.e., the lower limit is different for each sample) provide substantial movable space for the fluid. Previously, relaxation rate that related to conversion from T 2 spectra of porous media to pore size distributions is often regarded as constant as well as thickness of film fluid that produces pore surface relaxation. However, our work indicates that both the relaxation rate and thickness of film fluid are variables as a function of the pore radius. This study enriches the application of movable fluid properties in tight reservoirs formations, especially shales, and lays a foundation for tight porous media research using LF-NMR in future. • Movable fluid saturation indicates the exploitable rate of reservoir. • Movable fluid porosity is used to calculate the exploitable degree of reservoir. • The newly proposed method first employs nonconstant ρ 2 and h. • ρ 2 and h increase predictably as pore radius increases.

Review of Data Science Trends and Issues in Porous Media Research With a Focus on Image‐Based Techniques

AbstractData science as a flourishing interdisciplinary domain of computer and mathematical sciences is playing an important role in guiding the porous material research streams. In the present narrative review, we have examined recent trends and issues in data‐driven methods used in the image‐based porous material research studies relevant to water resources researchers and scientists. Initially, the recent trends in porous material data‐related issues have been investigated through search engine queries in terms of data source, data storage hub, programing languages, and software packages. Subsequent to a diligent analysis of the existing trends, a review of the common concepts of porous material research and data science are presented through six categories comprising big data, data regression, classification, image segmentation, geometry reconstruction, and image data resolution. We provide: (a) a focus on image‐based and pore scale methods which has not been presented previously, (b) a detailed search engine research for trend investigation, and (c) practical examples and comparison of data storage in porous media image‐based research. By reading this review article, an overall image of the active and popular interdisciplinary research domains can be obtained. Readers will also be informed of the latest data‐driven efforts and recommended research directions for tackling the image‐based porous material problems relevant to water resources research. We concluded that porous material image reconstruction and resolution improvement techniques are unique means to reveal unprecedented details of micro‐structures that may have been missed in a medium quality tomography image.

3D Printing for Experiments in Petrophysics, Rock Physics, and Rock Mechanics: A Review

Summary Geoscientific and engineering experiments in petrophysics, rock physics, and rock mechanics depend on multiple, costly, and sometimes rare samples used to characterize the properties of natural rocks. Testing these samples helps in modeling various hydrocarbon recovery and stimulation scenarios, as well as understanding the fluid-rock interactions in the subsurface under various pressure and temperature conditions. Over the last decade, 3D printing has matured to become a more commonly available tool to enable repeatable experiments with controllable materials and pore system geometries to investigate petrophysical, geomechanical, and geophysical properties of porous rocks. This review introduces the development, characteristics, and capabilities of 3D printing technology that are specifically used in research. Applications in the realm of petrophysics highlight the issues of replicating the pore network geometry and subsurface physics, aiming at understanding fluid flow in porous media problems. Using 3D-printed models in rock mechanics experiments focuses on generating comparable geomechanical properties and reproducing fractures, joint surfaces, and other rock structures, whereas in rock physics, geophysical forward modeling is highlighted to take advantage of 3D printing technology. By summarizing the recent advances in 3D printing as applied to petrophysics, rock physics, and rock mechanics, this review paper presents the current state of the art and the challenges in scale, cost, time, and materials, as well as the directions for advancing this frontier discipline to answer various fundamental questions regarding porous media research using 3D printing technology.

Advances in multiscale numerical and experimental approaches for multiphysics problems in porous media

Research on the scientific and engineering problems of porous media has drawn increasing attention in recent years. Digital core analysis technology has been rapidly developed in many fields, such as hydrocarbon exploration and development, hydrology, medicine, materials and subsurface geofluids. In summary, science and engineering research in porous media is a complex problem involving multiple fields. In order to encourage communication and collaboration in porous media research using digital core technology in different industries, the 5th International Conference on Digital Core Analysis & the Workshop on Multiscale Numerical and Experimental Approaches for Multiphysics Problems in Porous Media was held in Qingdao from April 18 to 20, 2021. The workshop was jointly organized by the China InterPore Chapter, the Research Center of Multiphase Flow in Porous Media at the China University of Petroleum (East China) and the University of Aberdeen with financial support from the National Sciences Foundation of China and the British Council. Due to the current pandemic, a hybrid meeting was held (participants in China met in Qingdao, while other participants joined the meeting online), attracting more than 150 participants from around the world, and the latest multi-scale simulation and experimental methods to study multi-field coupling problems in complex porous media were presented. Cited as: Yang, Y., Zhou, Y., Blunt, M. J., Yao, J., Cai, J. Advances in multiscale numerical and experimental approaches for multiphysics problems in porous media. Advances in Geo-Energy Research, 2021, 5(3): 233-238, doi: 10.46690/ager.2021.03.01

On the Importance of Incorporating Structural Heterogeneity of Porous Electrodes in LI-ION Battery Models: Pore Network Modelling a Way to MOVE Forward

Image processing of 3D tomographic images to extract structural information of porous materials has become extremely important in porous media research with the commoditization of x-ray tomography equipment to the lab scale. Extracted pore networks from image analysis techniques enable transport properties calculation for bigger domains at a very low computational cost, allowing substantial investigation of porous media. Consequently, they are being increasingly used to model Multiphysics transport processes in various energy storage devices like fuel cells and batteries, where the geometrical structure plays a vital role in electrochemical performance, but true pore-scale modeling is computationally infeasible.Traditionally, pore network modelling has been used to find electrochemical performance of Li Ion batteries by investigating pore phase interconnectivity in carbon electrodes. The actual lithium ion electrode, however, consists of active material, carbon binder and electrolyte filled pore phase. The interconnectivity of these phases influences the effective transport properties and hence electrochemical performance of cathode material. The presence of carbon binder phase not only reduces the SEI layer between active material and electrolyte phase but also influences tortuosity and effective electronic conductivity in the pore and solid phase respectively. The present work uses a pore network modelling frame work to investigate the effect of carbon binder phase on Lithium Ion battery cathode. We used actual three phase, X-ray tomography image of NMC-811 cathode material and studied its electrochemical performance with and without binder phase. Unlike previous models which compensate the importance of carbon binder as electrical conductor by assuming high electronic conductivity of active material, this study considers actual values of conductivities in all phases. Moreover, the impact of nanoporosity in the carbon binder phase was also explored and found to enhance the reaction rate compared to solid binder. The reduction in computational time achieved using the pore-network approach was so significant that adding additional physics and transient conditions could conceivably be included to increase the accuracy of the model, thereby providing more realistic simulations and point to the true limiting processes. The developed pore network model opens a new avenue for modelling complex electrochemical systems with less computational cost, enabling simulation of bigger electrode domains while keeping structural heterogeneities of material.

Stimulated echo double diffusion encoded imaging of closed pores: Influence and removal of unbalanced terms.

Nuclear magnetic resonance (NMR) diffusion pore imaging has been proposed to study the shape of arbitrary closed pores filled with an NMR-detectable medium by use of nonclassical diffusion encoding schemes. Potential applications can be found in biomedical imaging and porous media research. When studying non-point-symmetric pores, NMR signals with nonvanishing imaginary parts arise containing the pore shape information, which is lost for classical diffusion encoding schemes. Key limitations are the required high magnetic field gradient amplitudes and T2 relaxation while approaching the diffusion long-time limit. To benefit from the slower T1 decay, we demonstrate the feasibility of diffusion pore imaging with stimulated echoes using Monte Carlo simulations and experiments with hyperpolarized xenon-129 gas in well-defined geometries and show that the necessary complex-valued signals can be acquired. Analytical derivation of the stimulated echo double diffusion encoded signal was performed to investigate the effect of the additionally arising undesired terms on the complex phase information. These terms correspond to signals arising for spin-echo sequences with unbalanced gradients. For most possible applications, the unbalanced terms can be neglected. If non-negligible, selection of the appropriate signal component using a phase cycling scheme was demonstrated experimentally. Using stimulated echoes may be a step towards application of diffusion pore imaging to larger pores with gradient amplitudes available today in preclinical systems.

Development of Open-Source Porous Media Simulators: Principles and Experiences

By its very nature, research into multi-physical processes occurring in porous and fractured media requires a collaborative approach. An interdisciplinary approach has led to the adoption of collaborative software development paradigms in this field relying on software for scientific computing as research infrastructures. The development of open-source software has become a cornerstone of computational approaches in academia and has even spawned successful business models in the commercial world. This article is geared toward readers who want to learn more about potential benefits of open-source software in porous media research and who want to familiarize themselves with typical workflows required to become an active contributor to or user of open-source solutions for porous media simulation. The article puts general principles, motivations and concepts into the specific context of experiences and lessons learned from the authors developing the open-source software projects OpenGeoSys and DuMu $$^{\text {x}}$$ .

Dual network extraction algorithm to investigate multiple transport processes in porous materials: Image-based modeling of pore and grain scale processes

Image processing of 3D tomographic images to extract structural information of porous materials has become extremely important in porous media research with the commoditization of x-ray tomography equipment to the lab scale. Extracted pore networks from images using image analysis techniques enable transport properties calculation for bigger domains at a low computational cost, allowing pore-scale investigation of porous media over meaningful macroscopic length scales. The present study reports a pore network extraction algorithm to simultaneously extract void and solid networks from tomographic images of porous materials using simple image analysis techniques. Crucially, it includes connectivity and geometrical information of both void and solid phases as well as the interlinking of these phases with each other. Validation was obtained on networks extracted from simple cubic and random sphere packings over a range of porosities. The effective diffusivity in the void phase and thermal conductivity in the solid phase was then calculated and found to agree well with direct numerical simulation results on the images, as well as a range of experimental data. One important outcome of this work was a novel and accurate means of calculating interfacial areas between grains and voids directly from digital images, which is critical to many phenomena where phase interactions occur. The efficient ‘dual network’ algorithm is written in PYTHON using open source tools and provides a new way to study critical processes that depend on transport in both void and solid phase such as catalytic reactors and electrochemical systems.

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

An elementary question in porous media research is in regard to the relationship between structure and function. In most fields, the porosity and permeability of porous media are properties of key interest. There is, however, no universal relationship between porosity and permeability since not only does the fraction of void space matter for permeability but also the connectivity of the void fraction. With the evolution of modern day X-ray microcomputed tomography (micro-CT) and advanced computing, it is now possible to visualize porous media at an unprecedented level of detail. Approaches in analyzing micro-CT data of porous structures vary in the literature from phenomenological characterization to network analysis to geometrical and/or topological measurements. This leads to a question about how to consistently characterize porous media in a way that facilitates theoretical developments. In this effort, the Minkowski functionals (MF) emerge from the field of statistical physics where it is evident that many physical processes depend on the geometry and topology of bodies or multiple bodies in 3D space. Herein we review the theoretical basis of the MF, mathematical theorems and methods necessary for porous media characterization, common measurement errors when using micro-CT data and recent findings relating the MF to macroscale porous media properties. This paper is written to provide the basics necessary for porous media characterization and theoretical developments. With the wealth of information generated from 3D imaging of porous media, it is necessary to develop an understanding of the limitations and opportunities in this exciting area of research.

Prediction of fluid topology and relative permeability in imbibition in sandstone rock by direct numerical simulation

Pore-to-Darcy scale upscaling of multiphase flow is one of the major unresolved problems in many fields of porous media research. While this problem involves very fundamental aspects, there are many practical and application-driven challenges as well, such as the accurate prediction of Darcy-scale multiphase effective properties, e.g., relative permeability by pore-scale flow simulation on the basis of the imaged pore geometry, e.g., via X-ray computed micro-tomography. Validation of pore-scale modeling methods against experimental data by comparison of measured against simulated relative permeability curves has proven to be insufficient. Comparison of the fluid topology, in particular, the non-wetting phase topology, is a much more reliable criteria as relative permeability shows a very strong correlation with connectivity. While percolation-based quasi-static modeling approaches operating in the capillary limit have proven moderately successful in drainage, they largely fail to predict fluid connectivity in imbibition. We show that a fully coupled visco-capillary simulation, using a free-energy based lattice Boltzmann approach, correctly predicts fluid connectivity in imbibition, which was not predicted correctly by a quasi-static approach in a previous study on the same dataset. The respective relative permeability data shows a close match with results from Darcy-scale core flooding experiments while there is a major mismatch from quasi-static approach. The results once more highlight the close connection between relative permeability and fluid topology and suggest that topological measures are much more meaningful validation criteria for pore-scale simulation. The results also show that the direct simulation approach described in this paper, using appropriate initial and boundary conditions, correctly predicts Darcy-scale effective properties.

Versatile and efficient pore network extraction method using marker-based watershed segmentation.

Obtaining structural information from tomographic images of porous materials is a critical component of porous media research. Extracting pore networks is particularly valuable since it enables pore network modeling simulations which can be useful for a host of tasks from predicting transport properties to simulating performance of entire devices. This work reports an efficient algorithm for extracting networks using only standard image analysis techniques. The algorithm was applied to several standard porous materials ranging from sandstone to fibrous mats, and in all cases agreed very well with established or known values for pore and throat sizes, capillary pressure curves, and permeability. In the case of sandstone, the present algorithm was compared to the network obtained using the current state-of-the-art algorithm, and very good agreement was achieved. Most importantly, the network extracted from an image of fibrous media correctly predicted the anisotropic permeability tensor, demonstrating the critical ability to detect key structural features. The highly efficient algorithm allows extraction on fairly large images of 500^{3} voxels in just over 200 s. The ability for one algorithm to match materials as varied as sandstone with 20% porosity and fibrous media with 75% porosity is a significant advancement. The source code for this algorithm is provided.

Recent developments in neutron imaging with applications for porous media research

Abstract. Computed tomography has become a routine method for probing processes in porous media, and the use of neutron imaging is especially suited to the study of the dynamics of hydrogenous fluids, and of fluids in a high-density matrix. In this paper we give an overview of recent developments in both instrumentation and methodology at the neutron imaging facilities NEUTRA and ICON at the Paul Scherrer Institut. Increased acquisition rates coupled to new reconstruction techniques improve the information output for fewer projection data, which leads to higher volume acquisition rates. Together, these developments yield significantly higher spatial and temporal resolutions, making it possible to capture finer details in the spatial distribution of the fluid, and to increase the acquisition rate of 3-D CT volumes. The ability to add a second imaging modality, e.g., X-ray tomography, further enhances the feature and process information that can be collected, and these features are ideal for dynamic experiments of fluid distribution in porous media. We demonstrate the performance for a selection of experiments carried out at our neutron imaging instruments.