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DOE OSTI2024

BSDF Data generation for daylight applications: A call for international standardization

Standardized methods for generating angle-dependent, bidirectional, solar-optical properties for complex fenestration systems do not exist, which means that energy and daylight evaluations in building performance simulations often suffer from major inaccuracies. This position paper provides an overview of state-of-the-art data-driven methods for characterizing light scattering properties of fenestration materials and blind systems (e.g. fabrics, metal slats, patterned glazing), validation via laboratory, simulation and field tests, and salient issues in support of standardization of such methods via the International Standardization Organization (ISO). The ISO standard is intended to provide the fundamental underpinnings for recently mandated daylight standards that rely on bidirectional scattering distribution function data for climate-based daylight modelling and building performance simulations.

Geisler-Moroder, D.↗

DOE OSTI2020

Far-field thermal imaging below diffraction limit

Non-uniform self-heating and temperature hotspots are major concerns compromising the performance and reliability of submicron electronic and optoelectronic devices. At deep submicron scales where effects such as contact-related artifacts and diffraction limits accurate measurements of temperature hotspots, non-contact thermal characterization can be extremely valuable. In this work, we use a Bayesian optimization framework with generalized Gaussian Markov random field (GGMRF) prior model to obtain accurate full-field temperature distribution of self-heated metal interconnects from their thermoreflectance thermal images (TRI) with spatial resolution 2.5 times below Rayleigh limit for 530nm illumination. Finite element simulations along with TRI experimental data were used to characterize the point spread function of the optical imaging system. In addition, unlike iterative reconstruction algorithms that use ad hoc regularization parameters in their prior models to obtain the best quality image, we used numerical experiments and finite element modeling to estimate the regularization parameter for solving a real experimental inverse problem.

42 ENGINEERING↗

DOE OSTI2022

On the parameter combinations that matter and on those that do not: data-driven studies of parameter (non)identifiability

Abstract We present a data-driven approach to characterizing nonidentifiability of a model’s parameters and illustrate it through dynamic as well as steady kinetic models. By employing Diffusion Maps and their extensions, we discover the minimal combinations of parameters required to characterize the output behavior of a chemical system: a set of effective parameters for the model. Furthermore, we introduce and use a Conformal Autoencoder Neural Network technique, as well as a kernel-based Jointly Smooth Function technique, to disentangle the redundant parameter combinations that do not affect the output behavior from the ones that do. We discuss the interpretability of our data-driven effective parameters, and demonstrate the utility of the approach both for behavior prediction and parameter estimation. In the latter task, it becomes important to describe level sets in parameter space that are consistent with a particular output behavior. We validate our approach on a model of multisite phosphorylation, where a reduced set of effective parameters (nonlinear combinations of the physical ones) has previously been established analytically.

Evangelou, Nikolaos↗

DOE OSTI2021

Predicting Thermal Performance of an Enhanced Geothermal System From Tracer Tests in a Data Assimilation Framework

Abstract Predicting the thermal performance of an enhanced geothermal system (EGS) requires a comprehensive characterization of the underlying fracture flow patterns from practically available data such as tracer data. However, due to the inherent complexities of subsurface fractures and the generally insufficient geological/geophysical data, interpreting tracer data for fracture flow characterization and thermal prediction remains a challenging task. The present study aims to tackle the challenge by leveraging a data assimilation method to maximize the utilization of information inherently contained in tracer data, and meanwhile maintain the flexibility to handle various uncertainties. A tracer data interpretation framework was proposed with the following three components integrated: (a) We use principal component analysis (PCA) to reduce the dimensionality of model parameter space. (b) We use ES‐MDA (ensemble smoother with multiple data assimilation) to invert for fracture aperture/flow fields and obtain posterior model ensembles for uncertainty quantification. Various data types are assimilated jointly to improve the predictive ability of the posterior ensemble. (c) The inverted fracture aperture fields are then incorporated into reservoir models to predict thermal performance. We developed a field‐scale EGS model to verify the ability of the framework to characterize highly heterogeneous fracture aperture/flow fields and predicting thermal performance. We also applied the framework to a mesoscale field experiment to demonstrate its potential application in real‐world geothermal reservoirs. The results indicate that the proposed framework can effectively retrieve fracture flow information from tracer data for thermal prediction and uncertainty quantification, and thus provide informative guidance for EGS optimization and risk management.

15 GEOTHERMAL ENERGY↗

DOE OSTI2017

Empirical Profiling of Cold Hydrogen Plumes Formed from Venting Of LH2 Storage Vessels: Preprint

Liquid hydrogen (LH2) storage is a viable approach to assuring sufficient hydrogen capacity at commercial fuelling stations. Presently, LH2 is produced at remote facilities and then transported to the end-use site by road vehicles (i.e., LH2 tanker trucks). Venting of hydrogen to depressurize the transport storage tank is a routine part of the LH2 delivery process. The behaviour of cold hydrogen plumes has not been well-characterized because empirical field data is essentially non-existent. The NFPA 2 Hydrogen Storage Safety Task Group, which consists of hydrogen producers, safety experts, and CFD modellers, has identified the lack of understanding of hydrogen dispersion during LH2 venting of storage vessel as a critical gap for establishing safety distances at LH2 facilities, especially commercial hydrogen fuelling stations. To address this need, the NREL sensor laboratory, in collaboration with the NFPA 2 Safety Task Group developed the Cold Hydrogen Plume Analyzer to empirically characterize the hydrogen plume formed during LH2 storage tank venting. A prototype Analyzer was developed and field-deployed at an actual LH2 venting operation with critical findings that included: - H2 being detected as much as 2 m lower than the release point, which is not predicted by existing models - A small and inconsistent correlation between oxygen depletion and the hydrogen concentration - A negligible to non-existent correlation between in-situ temperature and the hydrogen concentration The Analyzer is currently being upgraded for enhanced metrological capabilities including improved real-time spatial and temporal profiling of the plume and tracking of prevailing weather conditions. Additional deployments are planned to monitor plume behaviour under different wind, humidity, and temperatures. This data will be shared with the NFPA 2 Safety Task Group and ultimately will be used support theoretical models and code requirements prescribed in NFPA 2.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2024

Chemical signature characterization with hyperspectral imagery: novel deep learning model architectures and physically-motivated data augmentation techniques

The high spectral resolution afforded by Hyperspectral Imaging (HSI) sensors is poised to bring unprecedented advancements to signature characterization applications. Thus far, much of the research in the machine learning field devoted to HSI applications has focused on a few specific tasks like land-use land-cover classification. In land classification tasks, spatial information is very important, and model architectures are often designed to leverage spatial contexts. However, it is unclear how well these spatially-tuned models will translate to tasks where spectral information is critical, like the detection and characterization of chemicals. In this work, we compare spectral models (inputs are 1D spectra) and spatial-spectral models (inputs are 3D cubes) in the context of predicting chemical concentration maps. We find that spatial-spectral models perform the best, though we find a wide range in performance across the different architectures tested. Additionally, we find that model performance is impacted by the availability of training data, particularly in scenarios where the training data doesn't fully capture the true variance of real-world conditions. We find that data augmentation can help mitigate sparse coverage of observed parameter space (e.g., seasonal or geographic variability in ground cover), and present augmentation strategies that are tailored to hyperspectral data.

• Artificial intelligence (AI) / machine learning ↗

DOE OSTI2018

Defining Modeling and Analysis Complexity Needs for Remediation - 18425

Many subsurface remedy decisions rely on model predictions or other types of data analyses to support site characterization and remedy selection, design, implementation, performance assessment, and closure. Thus, determining the appropriate level of complexity for modeling and data analysis is important to facilitate subsurface remedy decisions. Multiple factors are important in selecting the appropriate level of complexity in modeling and data analysis. For instance, the type and number of inputs required typically increases with increased complexity of analysis, and the ability to obtain or verify those inputs needs to be considered. The approach needs to establish the relationship between parameters or processes and contaminant transport and the detail of the description needed to meet the analysis objectives. Assessing the modeling complexity associated with the appropriate level of detail can be facilitated by considering complexity of the 1) modeling approach, 2) description of process, and 3) description of heterogeneity. As demonstrated by case studies, using a framework to guide determination of modeling and analysis complexity facilitates selection of the appropriate approach for the site situation. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Combinatorial Synthesis and High-Throughput Characterization of Microstructure and Phase Transformation in Ni–Ti–Cu–V Quaternary Thin-Film Library

Ni–Ti–based shape memory alloys (SMAs) have found widespread use in the last 70 years, but improving their functional stability remains a key quest for more robust and advanced applications. Named for their ability to retain their processed shape as a result of a reversible martensitic transformation, SMAs are highly sensitive to compositional variations. Alloying with ternary and quaternary elements to fine-tune the lattice parameters and the thermal hysteresis of an SMA, therefore, becomes a challenge in materials exploration. Combinatorial materials science allows streamlining of the synthesis process and data management from multiple characterization techniques. In this study, a composition spread of Ni–Ti–Cu–V thin-film library was synthesized by magnetron co-sputtering on a thermally oxidized Si wafer. Composition-dependent phase transformation temperature and microstructure were investigated and determined using high-throughput wavelength dispersive spectroscopy, synchrotron X-ray diffraction, and temperature-dependent resistance measurements. Of the 177 compositions in the materials library, 32 were observed to have shape memory effect, of which five had zero or near-zero thermal hysteresis. These compositions provide flexibility in the operating temperature regimes that they can be used in. A phase map for the quaternary system and correlations of functional properties are discussed with respect to the local microstructure and composition of the thin-film library.

42 ENGINEERING↗

DOE OSTI2017

Cluster 1: commercializing additive manufacturing—hurdles in materials characterization and testing

A major challenge in the commercialization of additive manufactured (AM) materials and processes is the ability to achieve acceptance of processes and products. There has been some progress towards acceptance has been made by adapting legacy qualification paradigms to match with the very limited process control and monitoring offered by AM machines. The opportunity for in-situ measurement can provide process monitoring and control perhaps changing the way we qualify parts however it is limited by lack of adequate process measurement methods. New measurement techniques, sensors and correlations to relevant phenomena are needed that enable process control and monitoring for consistently producing high quality articles. Beyond process data we need to characterize uncertainties of performance in all aspects of material, process and final part. These are prerequisites to achieving articles that are indeed worthy of materials characterization efforts that establish a microstructural reference of desirable performance through process-structure-property relations. Only then can industry apply physics based understanding of the material, part and process to probabilistically predict performance of an AM part. Our paper provides a brief overview, discussion of hurdles and key areas where R&D investment is needed.

36 MATERIALS SCIENCE↗

DOE OSTI2016

The first potential energy surfaces for the C{sub 6}H{sup −}–H{sub 2} and C{sub 6}H{sup −}–He collisional systems and their corresponding inelastic cross sections

Molecular anions have recently been detected in the interstellar and circumstellar media. Accurate modeling of their abundance requires calculations of collisional data with the most abundant species that are usually He atoms and H{sub 2} molecules. In this paper, we focus on the collisional excitation of the first observed molecular anion, C{sub 6}H{sup −}, by He and H{sub 2}. Theoretical calculations of collisional cross sections rely generally on ab initio interaction potential energy surfaces (PESs). Hence, we present here the first PESs for the C{sub 6}H{sup −}–H{sub 2} and C{sub 6}H{sup −}–He van der Waals systems. The ab initio energy data for the surfaces were computed at the explicitly correlated coupled cluster with single, double, and scaled perturbative triple excitations level of theory. The method of interpolating moving least squares was used to construct 4D and 2D analytical PESs from these data. Both surfaces are characterized by deep wells and large anisotropies. Analytical models of the PESs were used in scattering calculations to obtain cross sections for low-lying rotational transitions. As could have been anticipated, important differences exist between the He and H{sub 2} cross sections. Conversely, no significant differences exist between the collisions of C{sub 6}H{sup −} with the two species of H{sub 2} (para- and ortho-H{sub 2}). We expect that these new data will help in accurately determining the abundance of the C{sub 6}H{sup −} anions in space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Multimodal Chemical Characterization of Brown Carbon in Atmosphere and Snowpack during SAIL Field Campaign Report

The main goal of this field project was to characterize the chemical and physical properties of light-absorbing particles (LAP) present in the atmosphere and snowpack. The specific field efforts included: 1) online measurement and sampling of light-absorbing particles using a Magee Scientific model AE33 aethalometer (AE33) and a time-resolved aerosol collector (TRAC); and 2) sampling of snowpack during observed events of LAP deposition on snow, confirmed by onsite U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility measurements and meteorological data. The ongoing research objectives of our follow-up sample and data analysis include: 1) Characterization of aerosol regimes and sources, identifying particle-type populations, mixing states, and atmospheric transformations based on correlative analysis of our detailed chemical imaging and chemical characterization measurements and real-time records from the second ARM Mobile Facility (AMF2) and other instruments available from the Surface Atmosphere Integrated Field Laboratory (SAIL) experiment; 2) Assessment of the optical and chemical properties of snowpack deposits to investigate how aerosol deposition influences snowpack lifetime.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

WBS 2.1.5.401 - Model Validation and Site Characterization for Early Deployment MHK Sites and Establishment of Wave Classification Scheme

The "Resource Characterization" project delivers the data and tools needed to engineer robust marine renewable energy devices and projects. The project measures resource details at commercially promising sites, runs high resolution models of promising sites and regions, and develops classification schemes that streamline device engineering, project development, and increase investor confidence.

ENGINEERING,TIDAL AND WAVE POWER↗

DOE OSTI2016

Characterization of 3D Cirrus Cloud and Radiation Fields Using ARS/AIRS/MODIS data and its Application to Climate Model

During the report period, we have made the following research accomplishments. First, we performed analysis for a number of MODIS scenes comprising of heavy dust events and ice clouds, covering regions of frequent dust outbreaks in East Asia, Middle East, and West Africa, as well as areas associated with long-range dust transports over the Equatorial Tropical Atlantic Ocean. These scenes contain both dust/aerosols and clouds. We collected suitable aerosol/ice-cloud data, correlated ice cloud and aerosol parameters by means of statistical analysis, and interpreted resulting correlation trends based on the physical principles governing cloud microphysics. Aerosol and cloud optical depths and cloud effective particle size inferred from MODIS for selected domains were analyzed from which the parameters including dust aerosol number concentration, ice cloud water path, and ice particle number concentration were subsequently derived. We illustrated that the Twomey (solar albedo) effect can be statistically quantified based on the slope of best-fit straight lines in the correlation study. Analysis of aerosol and cloud retrieval products revealed that for all cases, the region with a larger dust aerosol optical depth is always characterized by a smaller cloud particle size, consistent with the Twomey hypothesis for aerosol-cloud interactions. Second, we developed mean correlation curves with uncertainties associated with small ice-crystal concentration observations for the mean effective ice crystal size (De) and ice water content (IWC) by dividing the atmosphere into three characteristic regions: Tropics cirrus, Midlatitude cirrus, including a temperature classification to improve correlation, and Arctic ice clouds. We illustrated that De has a high correlation with IWC based on theoretical consideration and analysis of thousands of observed ice crystal data obtained from a number of ARM-DOE field campaigns and other experiments. The correlation has the form: ln(De) = a + b ln(IWC) + c ((ln(IWC))2, where a, b, and c are fitting coefficients and are functions of three regions. We demonstrated that this correlation can be effectively incorporated in GCMs and climate models that predict IWC - a significant advance in ice microphysics parameterization for interactive cloud-radiation analysis and feedback. Substantial July mean differences are shown in the OLR (W/m2) and precipitation (mm/day) patterns between UCLA GCM simulations based on Des determined from the De-IWC correlations and the control run using a fixed ice crystal size. Third, in order to improve the computation of spectral radiative transfer processes in the WRF model, we developed a consistent and efficient radiation scheme that can better resolve the spectral bands, determine the cloud optical properties, and provide more reliable and accurate radiative heating fields. In the newly developed radiation module, we have implemented in WRF a modified and improved version referred to as the Fu-Liou-Gu scheme, which includes a combination of delta-four-stream and delta-two-stream approximations for solar and IR flux calculations, respectively. This combination has been proven to be computationally efficient and at the same time to produce a high degree of accuracy. The incorporation of nongray gaseous absorption in multiple scattering atmospheres was based on the correlated k-distribution method. The solar and IR spectra are divided into 6 and 12 bands, respectively, according to the location of absorption bands of H2O, CO2, O3, CH4, N2O, and CFCs. We further included absorption by the water vapor continuum and a number of minor absorbers in the solar spectrum leading to an additional absorption of solar flux in a clear atmosphere on the order of 1-3 W/m2. Additionally, we incorporated the ice microphysics parameterization that includes an interactive mean effective ice crystal size in association with radiation parameterizations. The Fu-Liou-Gu scheme is an ideal tool for the simulation of radiative transfer and ice microphysics within the domain of WRF. It is particularly useful for studying direct and indirect aerosol radiative effects associated with ice cloud formation. The newly implemented radiation module has been demonstrated to work well in WRF and can be effectively used for studies related to cirrus cloud formation and evolution as well as aerosol-cloud-radiation interactions. With the newly implemented radiation scheme, the simulations of cloud cover and ice water path have been improved for cirrus clouds, with a more consistent comparison with the corresponding MODIS observations, especially for optically thin cirrus with an improvement of about 20% in the simulated mean ice water path.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Collaborative Research: Vlasov-Maxwell Simulations to Resolve Electron Heating and Dissipation, in Quasi-Perpendicular Shocks (Final Report)

Collisionless shocks are a grand challenge problem in plasma physics and have been the subject of study for more than six decades. Shocks are ubiquitous phenomena in the universe and are responsible for transforming high energy and momentum flows into thermal energy and energetic particles. Understanding how shocks operate is of primary importance to understand the sun-earth coupling, protecting manned missions and spacecrafts from high energy particles, inertial confinement fusion, and radiation observed from astrophysical plasmas, such as supernova remnants and astrophysical jets. A major unanswered question on this frontier is, how does a collisionless plasma transform flow energy and momentum into electron thermal energy? Many potential mechanisms have been proposed to perform the conversion between flow and thermal energy in a collisionless plasma, but the answer has been elusive. To solve the mystery, non-relativistic perpendicular and quasi-perpendicular shocks are studied via a coordinated approach employing cutting-edge numerical simulations with the continuum kinetic code Gkeyll and spacecraft observations supplied by NASA's Magnetospheric Multiscale (MMS) mission. In both sets of data, we employ novel techniques to identify how and where energy is exchanged between electromagnetic fields and plasma particles. By combining cutting-edge spacecraft data and numerical simulations with these diagnostics, we have been able to identify some of the primary mechanisms responsible electron heating and dissipation in heliospheric shocks. In addition to electron dynamics, we have complete access to proton simulation and in situ data, enabling us to characterize proton heating and acceleration in quasi-perpendicular shocks. Many astrophysical quasi-perpendicular shocks have similar parameters to those in this study, including supernova remnants and shocks in galaxy clusters. Additionally, the simulation code, Gkeyll, and associated analysis scripts are openly available for community use. This award has supported a collaborative effort between Princeton University, the University of Arizona - Tucson, and the University of Maryland - College Park. The award funded three early career scientists, including the funding and training of one female postdoctoral researcher, who was involved in all aspects of the MMS data analysis and some portions of the simulation analysis.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2014

Particle dispersion in homogeneous turbulence using the one-dimensional turbulence model

Lagrangian particle dispersion is studied using the one-dimensional turbulence (ODT) model in homogeneous decaying turbulence configurations. The ODT model has been widely and successfully applied to a number of reacting and nonreacting flow configurations, but only limited application has been made to multiphase flows. Here, we present a version of the particle implementation and interaction with the stochastic and instantaneous ODT eddy events. The model is characterized by comparison to experimental data of particle dispersion for a range of intrinsic particle time scales and body forces. Particle dispersion, velocity, and integral time scale results are presented. The particle implementation introduces a single model parameter β{sub p}, and sensitivity to this parameter and behavior of the model are discussed. Good agreement is found with experimental data and the ODT model is able to capture the particle inertial and trajectory crossing effects. These results serve as a validation case of the multiphase implementations of ODT for extensions to other flow configurations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2019

Mitovirus and Mitochondrial Coding Sequences from Basal Fungus Entomophthora muscae

Fungi constituting the Entomophthora muscae species complex (members of subphylum Entomophthoromycotina, phylum Zoopagamycota) commonly kill their insect hosts and manipulate host behaviors in the process. In this study, we made use of public transcriptome data to identify and characterize eight new species of mitoviruses associated with several different E. muscae isolates. Mitoviruses are simple RNA viruses that replicate in host mitochondria and are frequently found in more phylogenetically apical fungi (members of subphylum Glomeromyoctina, phylum Mucoromycota, phylum Basidiomycota and phylum Ascomycota) as well as in plants. E. muscae is the first fungus from phylum Zoopagomycota, and thereby the most phylogenetically basal fungus, found to harbor mitoviruses to date. Multiple UGA (Trp) codons are found not only in each of the new mitovirus sequences from E. muscae but also in mitochondrial core-gene coding sequences newly assembled from E. muscae transcriptome data, suggesting that UGA (Trp) is not a rarely used codon in the mitochondria of this fungus. The presence of mitoviruses in these basal fungi has possible implications for the evolution of these viruses.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2015

The development of high-performance alkali-hybrid polarized He 3 targets for electron scattering

We present the development of high-performance polarized ³He targets for use in electron scattering experiments that utilize the technique of alkali-hybrid spin-exchange optical pumping. We include data obtained during the characterization of 24 separate target cells, each of which was constructed while preparing for one of four experiments at Jefferson Laboratory in Newport News, Virginia. The results presented here document dramatic improvement in the performance of polarized ³He targets, as well as the target properties and operating parameters that made those improvements possible. Included in our measurements were determinations of the so-called X-factors that quantify a temperature-dependent and as-yet poorly understood spin-relaxation mechanism that limits the maximum achievable ³He polarization to well under 100%. The presence of this spin-relaxation mechanism was clearly evident in our data. We also present results from a simulation of the alkali-hydrid spin-exchange optical pumping process that was developed to provide guidance in the design of these targets. Good agreement with actual performance was obtained by including details such as off-resonant optical pumping. Now benchmarked against experimental data, the simulation is useful for the design of future targets. Included in our results is a measurement of the K- ³He spin-exchange rate coefficient $k^\mathrm{K}_\mathrm{se} = \left ( 7.46 \pm 0.62 \right )\!\times\!10^{-20}\ \mathrm{cm^3/s}$ over the temperature range 503 K to 563 K.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2019

Subtask 1.1 – Advanced Characterization of Unconventional Oil and Gas Reservoirs to Enhance CO2 Storage Resource Estimates

The Energy & Environmental Research Center (EERC) developed methods and approaches for advanced characterization of unconventional oil and gas reservoirs to enhance CO2 storage resource estimates using samples collected from organic-rich shales of the Bakken Formation. The project was funded under U.S. Department of Energy (DOE) Cooperative Agreement No. DE-FE0024233, with nonfederal cost share provided by Hitachi High Technologies America. The project participants included the EERC, Hitachi High Technologies America, the team at the National Energy Technology Laboratory’s (NETL’s) computed tomography (CT) scanning laboratory in Morgantown, West Virginia, and several NETL staff who have worked on CO2 storage resource estimation. Investigations on the use of subsurface geologic formations for CO2 storage have been ongoing for many years. In the last decade, improvements in directional drilling technology and hydraulic fracturing of organic-rich shale and other tight rock formations have opened new opportunities for CO2 storage, with the potential cobenefit of enhancing gas and oil recovery. Gas-bearing, organic-rich shales have been of particular interest as a carbon storage target because their ability to store large volumes of CO2 is implied by their ability to store methane; however, estimating the CO2 storage resource of organic-rich shales and other unconventional reservoirs is challenging because of the lack of detailed geologic and petrophysical data that are needed to develop improved volumetric equations. According to NETL’s method for estimating regional-scale CO2 storage resources, the key factors that control CO2 storage in geologic formations include void space within induced fractures, natural fractures and matrix pores, as well as the occurrence and distribution of organic matter (OM) and clay minerals. A key goal of this effort was to use field emission scanning electron microscopy (FESEM) analysis of fine-grain unconventional reservoir rocks coupled with advanced image analysis and machine learning techniques to quantify the occurrence and distribution of porosity, OM, and clay minerals. By better understanding the potential migration pathways of CO2 within tight rocks and quantifying the occurrence of sorptive components (OM and clays) along potential flow pathways, methods to estimate the CO2 storage resource of such rocks can be refined. The Bakken petroleum system (Bakken) was used as the source of rock samples for this effort because it includes organic-rich shales with contrasting degrees of thermal maturity as well as tight, nonshale clastic rocks with varying clay mineral content. To better understand the key components of interest in tight rock samples, the EERC worked closely with Hitachi to optimize the FESEM settings necessary to resolve the sample mineralogy, porosity and OM content at micro- to nanoscale resolution. The EERC also worked closely with Hitachi to apply and improve its advanced mineral identification and characterization system (AMICS) software so that it is better suited for mineral identification at the high resolution necessary to resolve features of interest in fine-grained rocks such as organic-rich shales. The EERC also applied advanced image analysis software packages, including Ilastik, to classify and quantify the key components of interest within the samples using FESEM imagery. These advanced techniques allowed for quantification and differentiation of the void space within fractures, pores and OM, as well as a technique to determine and quantify the key mineral and OM components surrounding pore spaces. Being able to better quantify the void space and the potential accessibility of CO2 to sorptive minerals or OM within tight rocks allows for improved estimation of the CO2 storage resource potential of organic-rich shales and other unconventional formations. The information derived from the image analysis techniques was used to refine NETL’s method for estimating regional-scale CO2 storage resources in collaboration with staff from NETL. An additional goal of this work was to better understand rates of CO2 permeation, flow pathways, and sorption in organic-rich shales and nonshale reservoir rocks and to evaluate potential changes in CO2 flow over time. The EERC conducted long-duration (several weeks) CO2 flow-through testing at reservoir conditions using Bakken shale and nonshale samples to assess CO2 permeation rates and retention/sorption within core plugs to represent bulk matrix behavior. Conventional isothermal adsorption testing over a range of pressure conditions was also performed on pulverized Bakken samples to better assess potential maximum sorptive capacity of the samples. The results of this testing illustrated the higher sorptive capacity of the Bakken shales when compared to the sorptive capacity of the nonshale reservoir rock, which contains very little OM when compared to the Bakken shales. The results also illustrate the differences in permeation behavior as a function of fracture flow versus matrix flow. In an attempt to visualize CO2 permeation and flow behavior in Bakken shale samples over time, the EERC collaborated with NETL’s CT Lab in Morgantown, West Virginia. A variety of tests were performed at the NETL lab on unfractured and fractured samples of the Bakken shale. The unfractured Bakken shale samples did not result in CO2 migration within the samples over the testing duration; thus imagery of CO2 migration pathways within the matrix of Bakken samples was not obtained. The results of the flow-through testing within fractured samples illustrate changes in CO2 permeation rates over time, with a trend of decreasing CO2 flow over time. The results of this effort suggest that FESEM analysis coupled with advanced image analysis and machine learning techniques are effective in characterizing and quantifying the mineralogy, porosity, fractures, and OM content of 2-D imagery at the high resolution needed to identify features of interest in tight rocks. In addition, the image analysis techniques allow for identification and quantification of different porosity types as well as the key mineral components and/or OM that CO2 would contact in the reservoir as it migrates through pores and fracture networks. This is key information needed to better estimate the CO2 storage resource potential of organic-rich shales and other tight reservoirs. Additional work is needed to determine the sorptive capacity of the individual sample components (OM and clay minerals) at reservoir conditions so that this approach and the refined CO2 storage resource equation can be applied to other shale and unconventional reservoirs. In addition, subsequent work should be performed to better understand the number of images needed to use stereological approaches to estimate the volume of sample components using 2-D imagery, to explore machine learning techniques to differentiate between induced vs. natural fractures, and to estimate the connectivity of pores and fracture networks using 2-D imagery. Being able to derive this information at the microscale would facilitate scaling up of the sample characterization data used for reservoir modeling and simulation to estimate the CO2 storage resource potential of tight formations.

03 NATURAL GAS↗