Spatial Distribution Properties Research Articles

Adsorbed Gas Storage Digital Twin

One possibility for energy storage are fuels. With gaseous fuels like hydrogen or methane, significant efforts are necessary for a feasible storage in terms of compression or liquefaction. This is of particular importance in the mobility sector. An alternative to high-pressure or cryogenic gas storage is the storage by adsorption in porous media using nano-carbons, metal–organic frameworks, or metal hydrides as adsorbents. In order to assess the performance of the charging and discharging of adsorption tanks, the mass and energy balance as well as the phase equilibrium (adsorption isotherm) and, if present, the spatial distribution of properties has to be considered. In order to simplify the analysis and prediction of these models, an attempt is made to develop digital twins based on machine learning. Neural networks and Gaussian process regression are applied to replace the system of coupled nonlinear and differential equations. The data basis used is generated by simulations. Thus, it is possible to easily predict the performance of a storage tank for different gases or to determine an optimum storage device (material selection and tank design).

Rapid specialization and stiffening of the primitive matrix in developing articular cartilage and meniscus.

Understanding early patterning events in the extracellular matrix (ECM) formation can provide a blueprint for regenerative strategies to better recapitulate the function of native tissues. Currently, there is little knowledge on the initial, incipient ECM of articular cartilage and meniscus, two load-bearing counterparts of the knee joint. This study elucidated distinctive traits of their developing ECMs by studying the composition and biomechanics of these two tissues in mice from mid-gestation (embryonic day 15.5) to neo-natal (post-natal day 7) stages. We show that articular cartilage initiates with the formation of a pericellular matrix (PCM)-like primitive matrix, followed by the separation into distinct PCM and territorial/interterritorial (T/IT)-ECM domains, and then, further expansion of the T/IT-ECM through maturity. In this process, the primitive matrix undergoes a rapid, exponential stiffening, with a daily modulus increase rate of 35.7% [31.9 39.6]% (mean [95% CI]). Meanwhile, the matrix becomes more heterogeneous in the spatial distribution of properties, with concurrent exponential increases in the standard deviation of micromodulus and the slope correlating local micromodulus with the distance from cell surface. In comparison to articular cartilage, the primitive matrix of meniscus also exhibits exponential stiffening and an increase in heterogeneity, albeit with a much slower daily stiffening rate of 19.8% [14.9 24.9]% and a delayed separation of PCM and T/IT-ECM. These contrasts underscore distinct development paths of hyaline versus fibrocartilage. Collectively, these findings provide new insights into how knee joint tissues form to better guide cell- and biomaterial-based repair of articular cartilage, meniscus and potentially other load-bearing cartilaginous tissues. STATEMENT OF SIGNIFICANCE: Successful regeneration of articular cartilage and meniscus is challenged by incomplete knowledge of early events that drive the initial formation of the tissues' extracellular matrix in vivo. This study shows that articular cartilage initiates with a pericellular matrix (PCM)-like primitive matrix during embryonic development. This primitive matrix then separates into distinct PCM and territorial/interterritorial domains, undergoes an exponential daily stiffening of ≈36% and an increase in micromechanical heterogeneity. At this early stage, the meniscus primitive matrix shows differential molecular traits and exhibits a slower daily stiffening of ≈20%, underscoring distinct matrix development between these two tissues. Our findings thus establish a new blueprint to guide the design of regenerative strategies to recapitulate the key developmental steps in vivo.

Global Mapping of Structures and Properties of Crystal Materials.

Understanding materials' composition-structure-function relationships is of critical importance for the design and discovery of novel functional materials. While most such studies focus on individual materials, we conducted a global mapping study of all known materials deposited in the Materials Project database to investigate their distributions in the space of a set of seven compositional, structural, physical, and neural latent descriptors. These two-dimensional materials maps along with their density maps allow us to illustrate the distribution of the patterns and clusters of different shapes, which indicates the propensity of these materials and the tinkering history of existing materials. We then overlap the material properties such as composition prototypes and piezoelectric properties over the background material maps to study the relationships of how material compositions and structures affect their physical properties. We also use these maps to study the spatial distributions of properties of known inorganic materials, in particular those of local vicinities in structural space such as structural density and functional diversity. These maps provide a uniquely comprehensive overview of materials and space and thus reveal previously undescribed fundamental properties. Our methodology can be easily extended by other researchers to generate their own global material maps with different background maps and overlap properties for both distribution understanding and cluster-based new material discovery. The source code for feature generation and generated maps is available at https://github.com/usccolumbia/matglobalmapping.

Assessment and Spatial Modelling of Agrochernozem Properties for Reclamation Measurements

Traditional land-use systems can be modified under the conditions of climate change. Higher air temperatures and loss of productive soil moisture lead to reduced crop yields. Irrigation is a possible solution to these problems. However, intense irrigation may have contributed to land degradation. This research assessed the ameliorative potential of soil and produced large-scale digital maps of soil properties for arable plot planning for the construction and operation of irrigation systems. Our research was carried out in the southern forest–steppe zone (Southern Ural, Russia). The soil cover of the site is represented by agrochernozem soils (Luvic Chernozem). We examined the morphological, physicochemical and agrochemical properties of the soil, as well as its heavy metal contents. The random forest (RF) non-linear approach was used to estimate the spatial distribution of the properties and produce maps. We found that soils were characterized by high organic carbon content (SOC) and neutral acidity and were well supplied with nitrogen and potassium concentrations. The agrochernozem was characterized by favorable water–physical properties and showed good values for water infiltration and moisture categories. The contents of heavy metals (lead, cadmium, mercury, cobalt, zinc and copper) did not exceed permissible levels. The soil quality rating interpretation confirms that these soils have high potential fertility and are convenient for irrigation activities. The spatial distribution of soil properties according to the generated maps were not homogeneous. The results showed that remote sensing covariates were the most critical variables in explaining soil properties variability. Our findings may be useful for developing reclamation strategies for similar soils that can restore soil health and improve crop productivity.

Coupling mechanisms between cement hydration and permafrost during well construction in the Arctic region

During well construction in the Arctic region, the coupling mechanisms between cement hydration and permafrost may lead to unpredictable thaw of permafrost, thereby threatening the wellbore stability and ecological environment in the Arctic. In the present study, thermal kinetics of cement hydration under different temperatures are studied experimentally, and the hydration kinetics model is proposed. Then the coupled model is proposed considering the complicated physicochemical interactions between cement and permafrost. In the proposed model, the hydration reaction of cement, phase change of the permafrost, and the water migration in permafrost are considered. Moreover, the coupling mechanisms between cement and permafrost are studied by numerical simulations. Temporal and spatial distribution of properties in permafrost are obtained including the temperature, and the water/ice content. The obtained results shows an obvious temperature increase and permafrost thaw due to cement hydration heat. Moreover, it is found that low-temperature ambient in permafrost slows down the cement hydration process. During the permafrost thaw, dissociated water migrates from the warm area near the cement to the far cold area. Compared with the hydration process, heat diffusion in permafrost takes a much longer period. Thus, continuous attention should be paid to threaten posed by permafrost thaw for a long period after well cementing.

Estimation of spatial uncertainty in material property distributions within heterogeneous structures using optimized convolutional neural networks

Variations in material behaviors within structures built with heterogeneous materials lead to damage initiation and evolution in locally weak regions. Quantifying the property variability within the structure and forward propagation of the impact of the material property uncertainty on the structural response is critical for reliability analysis and structural performance maximization. Commonly, quantification of the variability requires either computationally expensive high-fidelity models of the underlying microstructure or extensive experimental testing. In this paper, we model the uncertainty with spatially correlated random fields and calibrate the model parameters from limited strain field observations using Neural Networks (NNs). The calibration is performed by trained NNs, which outputs best-fit parameters for the spatial correlation model by accepting filtered Digital Image Correlation (DIC) strain distributions as the input. We demonstrate that by training the NNs using simulated data, the resulting networks are able to calibrate the spatial distribution uncertainty models effectively for a set of Fused Filament Fabrication (FFF) printed structures. The methodology requires a limited number of experimental datasets and produces fast estimations of the best-fit parameters of the uncertainty model compared to optimization or inverse fitting methods. This method allows experimentalists to use the same DIC information routinely obtained during modulus or strength testing to calibrate a spatial property distribution uncertainty model for the underlying microstructure.

Structural preconditions for adaptive urban areas: Framework rules, several property and the range of possible actions

Recent studies have highlighted the importance of urban design and planning strategies that trigger the formation of diverse and intricate urban areas that have the capacity to adapt and change spontaneously from the bottom up. Although this article does likewise, it proposes a different and still underexamined approach. It provides an exploratory lens for interpreting and assessing the propensity of urban areas to rely on self-adaptive processes of change, taking into account the impact of two key structural (institutional) aspects: planning rules and property ownership patterns. This article highlights what can ensue from different background preconditions, particularly in terms of urban self-adaptability. As the article demonstrates, not all urban areas are or can be self-adaptive and open to spontaneous processes of change to the same extent. This depends mainly on the type of planning rules (teleocratic vs nomocratic) and the spatial distribution of property (centralised vs several). Combined, these two aspects form the structural-institutional framework that defines the degree of self-adaptability of urban areas. This article contributes to this important topic in both explanatory and strategic terms.

A comparison between Kalman ensemble generator and geostatistical frequency-domain electromagnetic inversion: The impacts on near-surface characterization

The spatial distribution of the physical properties of the first meters beneath the earth’s surface is often complex due to its highly dynamic nature and small-scale heterogeneities resulting from natural and anthropogenic processes. Therefore, obtaining numerical 3D models that accurately describe the spatial distribution of these properties is often challenging yet essential for different fields such as environmental assessment and remediation, geoarchaeological conservation, and precision agriculture. Frequency-domain electromagnetic (FDEM) induction methods have proven their potential to image these properties in high (spatial) detail because FDEM measurements are sensitive to two key soil properties: electrical conductivity and magnetic susceptibility. Predicting subsurface properties from FDEM data requires solving an ill-posed and nonlinear inverse problem with multiple solutions. Recently, there has been a rapid growth of FDEM inversion methods, which may be broadly divided into probabilistic and deterministic methods. We compare two stochastic FDEM inversion approaches: the Kalman ensemble generator (KEG) and another one formulated as an iterative geostatistical FDEM inversion. Both methods are applied to a synthetic data set with spatially heterogeneous physical properties of interest, mimicking a real landfill mining site. The predicted models are compared with the reference models in terms of histogram and variogram models reproduction and in their ability to quantify spatial uncertainty. The results indicate the ability of both methods to predict the reference values. Although the KEG is computationally efficient, it struggles to reproduce the extreme values. In contrast, the geostatistical inversion approach ensures the reproduction of the imposed histograms and variogram models in the predicted models. As the prior information is included in both inversion methods in different ways, the pointwise variance models computed from all of the posterior models have different information. The synthetic data set is available to the community, so it can be used as a benchmark for other FDEM inversion methods.

The Di models method: geological 3-D modeling of detrital systems consisting of varying grain fractions to predict the relative lithological variability for a multipurpose usability

The coexistence of a wide variety of subsurface uses in urban areas requires increasingly demanding geological prediction capacities for characterizing the geological heterogeneities at a small-scale. In particular, detrital systems are characterized by the presence of highly varying sediment mixtures which control the non-constant spatial distribution of properties, therefore presenting a crucial aspect for understanding the small-scale spatial variability of physical properties. The proposed methodology uses the lithological descriptions from drilled boreholes and implements sequential indicator simulation to simulate the cumulative frequencies of each lithological class in the whole sediment mixture. The resulting distributions are expressed by a set of voxel models, referred to as Di models. This solution is able to predict the relative amounts of each grain fraction on a cell-by-cell basis and therefore also derive a virtual grain size distribution. Its implementation allows the modeler to flexibly choose both the grain fractions to be modeled and the precision in the relative quantification. The concept of information entropy is adapted as a measure of the disorder state of the clasts mixture, resulting in the concept of “Model Lithological Uniformity,” proposed as a measure of the degree of detrital homogeneity. Moreover, the “Most Uniform Lithological Model” is presented as a distribution of the most prevailing lithologies. This method was tested in the city of Munich (Germany) using a dataset of over 20,000 boreholes, providing a significant step forward in capturing the spatial heterogeneity of detrital systems and addressing model scenarios for applications requiring variable relative amounts of grain fractions.

Improving porosity and gamma-ray prediction for the Middle Jurassic Hugin sandstones in the southern Norwegian North Sea with the application of deep neural networks

The complete characterization of a reservoir requires accurate determination of properties such as the porosity, gamma ray, and density, among others. A common workflow is to predict the spatial distribution of properties measured by well logs to those that can be computed from the seismic data. In general, a high degree of scatter of data points is seen on crossplots between P-impedance and porosity, or P-impedance and gamma ray, suggesting great uncertainty in the determined relationship. Although for many rocks there is a well-established petrophysical model correlating the P-impedance to porosity, there is not a comparable model correlating the P-impedance to gamma ray. To address this issue, interpreters can use crossplots to graphically correlate two seismically derived variables to well measurements plotted in color. When there are more than two seismically derived variables, the interpreter can use multilinear regression or artificial neural network analysis that uses a percentage of the upscaled well data for training to establish an empirical relation with the input seismic data and then uses the remaining well data to validate the relationship. Once validated at the wells, this relationship can then be used to predict the desired reservoir property volumetrically. We have described the application of deep neural network (DNN) analysis for the determination of porosity and gamma ray over the Volve field in the southern Norwegian North Sea. After using several quality-control steps in the DNN workflow and observing encouraging results, we validate the final prediction of the porosity and gamma-ray properties using blind well correlation. The application of this workflow promises significant improvement to the reservoir property determination for fields that have good well control and exhibit lateral variations in the sought properties.

Normal-incidence bottom loss measurements over a sand-mud gradient and comparison to direct seabed measurements

Seabed variability introduced by physical or biogenic processes contributes to multi-scale horizontal and vertical variability in seabed acoustic and geotechnical properties. An experiment was conducted near the mouth of Mobile Bay to investigate such spatial variability over a mud-sand gradient. A normal-incidence bottom loss survey was conducted on a 750 × 1500 m2 grid with 50-m track-line spacing, in which a broadband acoustic source transmitted frequency-modulated chirps (1–100 kHz) every second, resulting in dense sampling of the seabed reflection coefficient over a wide frequency band. Diver cores were collected in the survey area for direct analysis of near-surface seabed properties. Cores were acoustically logged to obtain vertical profiles of sound speed and attenuation (10–1000 kHz) and then analyzed for physical properties (porosity, grain size, carbon content), infauna community composition, and sediment strength properties. Additionally, in situ portable free fall penetrometer measurements were conducted within the survey area to characterize geotechnical properties within the upper 50 cm of the sediment. Spatial distributions of properties estimated from bottom loss measurements (e.g. porosity and sound speed) will be compared with vertical profiles from the cores and from the penetrometer to examine potential connections between seabed acoustic, geotechnical, and biological parameters. [Sponsored by ONR.]

Maize genotype-specific exudation strategies: An adaptive mechanism to increase microbial activity in the rhizosphere

Plants stimulate microbial enzyme production in the rhizosphere, regulating soil organic matter decomposition and nutrient cycling. The availability of labile organic compounds (i.e. exudates) and water is the main prerequisite for such microbial activity and enzyme production, thus shaping the rhizosphere. Root morphology (i.e., root hairs) and exudate composition define the spatial distribution of properties and functions in the rhizosphere. However, the role of root architecture and exudate composition in this spatial self-organization of the rhizosphere remains unknown.We coupled three in situ imaging approaches: 14C imaging to localize exudates, soil zymography to analyze enzyme activity distribution, and neutron radiography for water fluxes to trace the spatial structure of the rhizosphere of three maize genotypes (wild-type, mutant with defective root-hair prolongation rth3, and mutant with reduced benzoxazinoid content in root exudates bx1). The co-localization of these three soil images revealed the pivotal role of both optimal water content (neutron radiography) and root exudation (14C imaging) for β-glucosidase production by the rhizosphere microbiome and its hydrolytic activity (zymography). Root hairs increased the exudate release and enlarged the spatial extent of increased β-glucosidase activity around the root axis by 35%, leading to a two-fold faster decomposition of 14C exudates compared to the mutant with defective root hairs. In contrast, benzoxazinoids suppressed β-glucosidase activity by 30%, reflecting decreased microbial activity, whereas their absence broadened the rhizosphere. Overall, root hairs in wild-type maize increased microbial activity (i.e. β-glucosidase production), whereas the benzoxazinoids in root exudates suppressed microorganisms.

An Automated Approach to Groundwater Quality Monitoring—Geospatial Mapping Based on Combined Application of Gaussian Process Regression and Bayesian Information Criterion

Sustainable management of the environment is based on the preservation of natural resources, first of all, freshwater—both surface and groundwater—from exhaustion and contamination. Thus, development of adequate monitoring solutions, including fast and adaptive modelling approaches, are of high importance. Recent progress in machine learning techniques provide an opportunity to improve the prediction accuracy of the spatial distribution of properties of natural objects and to automate all stages of this process to exclude uncertainties caused by handcrafting. We propose a technique to construct the weighted Water Quality Index (WQI) and the spatial prediction map of the WQI in tested area. In particular, WQI is calculated using dimensionality reduction technique (Principal Component Analysis), and spatial map of WQI is constructed using Gaussian Process Regression with automatic kernel structure selection using Bayesian Information Criterion (BIC). We validate our approach on a new dataset for groundwater quality in the New Moscow region, where groundwater is mostly used for drinking purposes. According to estimated WQI values, groundwater quality across the study region is relatively high, with few points, less than 0.5% of all observations, severely contaminated. Estimated WQIs then were used to construct spatial distribution models, GPR-BIC approach was compared with ordinary Kriging (OK), Universal Kriging (UK) with exponential, Gaussian, polynomial and periodic kernels. Quality of models was assessed using cross-validation scheme, according to which BIC-GPR approach showed better performance on average with 15% higher R2 score comparing to other Kriging models. We show that the proposed geospatial interpolation is a potentially powerful and adaptable tool for predicting the spatial distribution of properties of natural resources.

Multiresolution Grid Connectivity-Based Transform for Efficient History Matching of Unconventional Reservoirs

Summary Proper characterization of heterogeneous rock properties and hydraulic fracture parameters is essential for optimization of well spacing and reliable estimation of estimated ultimate recovery (EUR) in unconventional reservoirs. High resolution characterization of matrix properties and complex fracture parameters require efficient history matching of well production and pressure response. We propose a novel reservoir model parameterization method to reduce the number of unknowns, regularize the ill-posed problem, and enhance the efficiency of history matching of unconventional reservoirs. The proposed method makes a low-rank approximation of the spatial distribution of reservoir properties taking into account the varying model resolution of the matrix and hydraulic fractures. Typically, hydraulic fractures are represented with much higher resolution through local grid refinements compared to the matrix properties. In our approach, the spatial property distribution of both matrix and fractures is represented using a few parameters via a linear transformation with multiresolution basis functions. The parameters in transform domain are then updated during model calibrations, substantially reducing the number of unknowns. The multiresolution basis functions are constructed by using Eigen-decomposition of an adaptively coarsened grid Laplacian corresponding to the data resolution. Higher property resolution at the area of interest through the adaptive resolution control while keeping the original grid structure improves quality of history matching, reduces simulation runtime, and improves the efficiency of history matching. We demonstrate the power and efficacy of our method using synthetic and field examples. First, we illustrate the effectiveness of the proposed multiresolution parameterization by comparing it with traditional methods. For the field application, an unconventional tight oil reservoir model with a multistage hydraulic fractured well is calibrated using bottomhole pressure and water cut history data. The hydraulic fractures as well as the stimulated reservoir volume (SRV) near the well are represented with higher grid resolution. In addition to matrix and fracture properties, the extent of the SRV and hydraulic fractures are also adjusted through history matching using a multiobjective genetic algorithm. The calibrated ensemble of models are used to obtain bounds of production forecast. Our proposed method is designed to calibrate reservoir and fracture properties with higher resolution in regions that have improved data resolution and higher sensitivity to the well performance data, for example the SRV region and the hydraulic fractures. This leads to a fast and efficient history matching workflow and enables us to make optimal development/completion plans in a reasonable time frame.

THE STUDY OF THE PREDICTIVE ABILITY OF NUMERICAL AND ANALYTICAL MODELS (THE CASE OF MUTUAL WELL IMPACT EVALUATION)

The main purpose of any model is to provide an opportunity to study the model object and the processes running in it for obtaining the predictive characteristics, among other reasons. In this connection, it is important to know, which mathematical models can help in analyzing and supporting oil deposit development, in particular, in assessing the mutual influence of production and injection wells. The characteristic features of mathematical modeling of field development include the oil deposits being located in natural formations that cannot be directly observed, as well as the complex filtration processes taking place in the formations due to the formation structure. Therefore, the mathematical modeling of development can be both complex and simple. On the one hand, it may use complex numerical hydrodynamic models, based on the understanding of spatial distribution of reservoir properties, which have an opportunity for detailed description of filtration processes. On the other hand, the modeling may use relatively simple analytical models, which have no need to specify the spatial distribution of properties; yet, the description of filtration processes is significantly simplified in comparison with hydrodynamics. Therefore, the practical value of the modeling result depends on the right approach to modeling. The task of estimating the mutual influence of wells requires the choice of numerical or analytical model to be based on understanding of the predictive ability of the models under consideration. Since such ability depends both on the ability to describe filtration processes in detail and on the need to take into account the spatial distribution of reservoir properties, it is initially impossible to conclude, which model has the best predictive ability. It becomes possible to reveal the level of predictive ability when considering the problem of mutual well impact assessment for synthetic models of oil deposits. This article presents the results of studies in the case of ten synthetic models. Numerical hydrodynamic models and analytical CRM models were set up for “actual” data of well operation. Using the retrospective test method, the authors have shown that the analytical models have a higher predictive power than the numerical models.

Simultaneous upscaling of two properties of reservoirs in one dimension using adaptive bandwidth in kernel function method

Abstract Upscaling of primary geological models with huge cells, especially in porous media, is the first step in fluid flow simulation. Numerical methods are often used to solve the models. The upscaling method must preserve the important properties of the spatial distribution of the reservoir properties. An grid upscaling method based on adaptive bandwidth in kernel function is proposed according to the spatial distribution of property. This type of upscaling reduces the number of cells, while preserves the main heterogeneity features of the original fine model. The key point of the paper is upscaling two reservoir properties simultaneously. For each reservoir feature, the amount of bandwidth or optimal threshold is calculated and the results of the upscaling are obtained. Then two approaches are used to upscaling two properties simultaneously based on maximum bandwidth and minimum bandwidth. In fact, we now have a finalized upscaled model for both reservoir properties for each approach in which not only the number of their cells, but also the locations of the cells are equal. The upscaling error of the minimum bandwidth approach is less than that of the maximum bandwidth approach.

Identification and mapping of riverbed sediment facies in the Columbia River through integration of field observations and numerical simulations

AbstractIn the Hanford Reach of the Columbia River, a thin layer of recent alluvium overlies the sedimentary formations that comprise the unconfined groundwater aquifer. Experimental and modelling studies have demonstrated that this alluvial layer exerts significant control on the exchange of groundwater and surface water (hydrologic exchange flux), and is associated with elevated levels of biogeochemical activity. This layer is also observed to be strongly heterogeneous, and quantifying the spatial distribution of properties over the range of scales of interest is challenging. Facies are elements of a sediment classification scheme that groups complex geologic materials into a set of discrete classes according to distinguishing features. Facies classifications have been used as a framework for assigning heterogeneous material properties to grid cells of numerical models of flow and reactive transport in subsurface media. The usefulness of such an approach hinges on being able to relate facies to quantitative properties needed for flow and reactive transport modelling, and on being able to map facies over the domain of interest using readily available information. Although aquifer facies have been used in various modelling contexts, application of this concept to riverbed sediments is relatively new. Here, we describe an approach for categorizing and mapping recent alluvial (riverbed) sediments based on the integration of diverse observations with numerical simulations of river hydrodynamics. The facies have distinct distributions of sediment texture that correspond to variations in hydraulic properties, and therefore provide a useful framework for assigning heterogeneous properties in numerical simulations of hydrologic exchange flows and biogeochemical processes.

The Impact of Single-Family Rental REITs on Regional Housing Markets: A Case Study of Nashville, TN

The U.S. Congress authorized the creation of real estate investment trusts (REITs) in 1960 so companies could develop publically traded real estate investment portfolios. REITs focus on commercial property, retail property, and rental property. During the last decade, REITs became more active in regional housing markets across the U.S. Single-family rental (SFR) REITs have grown tremendously, buying up residential properties across the country. In some regional housing markets, SFR REITs own noticeable shares of single-family homes. In those settings, SFR REITs take large numbers of housing units off of real estate markets where homeownership transactions occur and manage these properties as part of commercial rental inventories. This has resulted in a new category of multiple property owners, composed of institutional investors as opposed to individual investors, which further exacerbates property wealth concentration and polarization. This study examines the socio–spatial distribution of properties in SFR REIT portfolios to determine if SFR REIT properties tend to cluster in distinct areas. This study will focus on the regional housing market in Nashville, TN. Nashville has one of the most active SFR REIT sectors in the country. County tax assessor records were used to identify SFR REIT properties. These data were joined with U.S. Census data to create a profile of communities. The data were analyzed using SPSS statistical software and GIS software. Our analysis suggests that neighborhoods with clusters of SFR REITs fit the SFR REIT business model. Clusters occur in communities with newer homes, residents with higher levels of educational attainment, and middle to upper-middle incomes. The paper concludes with several recommendations for future research on SFR REITs.

Modeling the in Situ Ionic and Electronic Conductivity of the Porous Electrodes Linked to the Oxidation-Reduction Reactions in Solid Oxide Fuel Cells

The ionic conductivity is essential to the performance of a solid oxide fuel cell (SOFC). While the effective conductivity of a porous electrode is frequently involved in SOFC models, the concept of the real (in situ) conductivity in a working electrode is yet to be clarified. To model the in situ ionic and electronic conductivity, the microstructure/geometry and the associated spatial property distribution, as well as the chemical reactions have to be explicitly considered. Here we present a theoretical framework together with phase field modeling to approach this issue. In addition, a recently developed spectral method is used to solve the transport problem in media with complex microstructure.

Importance of conceptual geological models in 3D reservoir modelling

Conceptual Geological Models (CGM’s) represent geological knowledge and are traditionally visualized by 2D geologic crosssections. Construction of CGMs is a non-linear and complex process which involves application of geological rules and experience. These models describe essential features of geological situations, illustrate the principal processes of the petroleum system, and provide important information about reservoir characteristics, pressure and fluid flow of the field under study. The CGMs are being extensively used as the key input for 3D reservoir modelling and simulation at different stages of E&P projects. A fully integrated CGM building process consists of at least four stages: construction of one or more structural models, identification of depositional models, construction of sedimentary facies models and finally building of diagenetic facies models. This process requires integration of geological, geochemical, geophysical, and petrophysical data along with information from well testing and production into a comprehensive model describing the physical features of the geologic system. This paper briefly describes the different components of CGM and the model building process. The importance of integrating CGM in the 3D reservoir models has been demonstrated through an example of siliciclastic reservoir. This reservoir has complex internal sedimentary distribution and stratigraphy which leads to high subsurface uncertainties. The study shows that different possible conceptual geologic scenarios provide significantly different Hydrocarbon-In-Place, recoverable resources and production forecasts. Therefore, the successful application of appropriate CGM’s is critical for better 3D reservoir characterization and modelling which enables us to identify and rank the key reservoir uncertainties and assess their impact, establish interdependency of spatial property distribution, make volumetric assessments, plan the number and location of wells including their drainage area, optimize recovery efficiency and obtain reliable production forecasts.