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DOE OSTI2025

Metagenome-assembled genomes from topsoils collected during NEON campaign in East River, CO (06/14/2018-06/28/2018)

The Watershed Function Science Focus Area (WF SFA) at Lawrence Berkeley National Lab is working to build a mechanistic understanding of the distribution and dynamics of biogeochemical processes in mountainous watersheds and their response to perturbation. In June 2018, the NEON (National Ecological Observatory Network) Airborne Observatory Platform (AOP) performed a taskable airborne imaging campaign to collect visible to shortwave infrared (VSWIR) imaging spectroscopy and LiDAR data across 330 km2 in the Upper East River at Crested Butte, CO. We conducted a parallel ground sampling campaign to sample vegetation traits, as well as soil physical, chemical, and microbiological characteristics. We collected these samples from 438 sites across 12 locations spanning much of the elevation, topographic, and geologic variability across the study area. A subset of 250 samples were used for soil metagenomics which is presented here. In addition, at each site, vegetation samples were collected to measure species-specific leaf water content and leaf mass area, foliar elemental composition and foliar CN stable isotope ratios. Soil samples were collected to measure soil physical properties which include bulk density and soil texture analysis. A suite of soil chemical properties was measured from the samples collected at each site, including pH, organic matter, concentrations exchangeable cations, total elemental composition, and the concentrations of extractable N pools (e.g. total free amino acids, ammonium, nitrate, dissolved organic N, and total dissolved N). Additionally, we have measured soil microbial biomass CN stoichiometry. Here, we present 1982 metagenome-assembled genomes (MAGs) for the bacterial and archaeal community from topsoil collected from during NEON 2018 campaign. All metagenomes were sequenced at JGI (Joint Genome Institute) (GOLD Study ID: Gs0149986). Metagenomes were assembled using JGI Metagenome Workflow (10.1128/mSystems.00804-20). The dataset includes (1) zip files for 1982 MAG fasta files (neon_genomes1-5.tar.gz, split into 5 tarballs to keep tarballs under 0.5 GB), (2) neon_Gs0149986_samples_soilproperties_metagenomes.csv: the sample information together with the accession numbers for the underlying metagenomes and the associated soil physical and chemical measurements in NMDC (National Microbiome Data Collaborative) compliant format, (3) neon_Gs0149986.kml: location bounding box file for the sampled locations, (4) samples.csv: sample metadata file used to register Internationall Generic Sample Numbers (IGSNs), (5) flmd.csv: file level metadata file, and (6) dd.csv: data dictionary file. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗

DOE OSTI2017

Measuring changes in ambient noise levels from the installation and operation of a surge wave energy converter in the coastal ocean

Ecosystem impacts resulting from elevated underwater noise levels generated by anthropogenic activities in the coastal ocean are poorly understood and remain difficult to address as a result of a significant gap in knowledge for existing nearshore sound levels. Ambient noise is an important habitat component for marine mammals and fish that use sound for essential functions such as communication, navigation, and foraging. Questions surrounding the amplitudes, frequency distributions, and durations of noise emissions from renewable wave energy conversion (WEC) projects during their construction and operation present concerns for long-term consequences in marine habitats. Oregon’s dynamic nearshore environment presents significant challenges for passive acoustic monitoring that include flow noise contamination from wave orbital motions, turbulence from breaking surf, equipment burial, and fishing pressure from sport and commercial crabbers. This project included 2 techniques for passive acoustic data collection: 1) campaign style deployments of fixed hydrophone lander stations to capture temporal variations in noise levels and 2) a drifting hydrophone system to record spatial variations within the project site. The hydrophone lander deployments were effective and economically feasible for enabling robust temporal measurements of ambient noise levels in a variety of sea state conditions. Limiting factors for the fixed stations included 1) a flow shield mitigation strategy failure in the first deployment resulting in significant wideband data contamination and 2) flow noise contamination of the unshielded sensors restricting valuable analysis to frequencies above 500 Hz for subsequent deployments. Drifting hydrophone measurements were also effective and economically feasible (although logistically challenging in the beginning of the project due to vessel time constraints) providing a spatial distribution of sound levels, comparisons of noise levels in varying levels of vessel traffic during similar sea states, and reducing the frequencies contaminated by flow noise to f < 50 Hz by an effective drifting hydrophone system design strategy. Results from this project can still assist regulatory agencies and WEC developers in permitting and licensing, reducing project costs overall and assisting the economic development of the WEC industry, thus furthering the MHK energy industry and easing the U.S. reliance on foreign oil for energy production. Additionally, results from this project can be used to help inform coastal resource managers and regulatory agencies on existing baseline noise level variability and ecosystem health.

16 TIDAL AND WAVE POWER↗

DOE OSTI2015

Structural Significance of Lipid Diversity as Studied by Small Angle Neutron and X-ray Scattering

We review recent developments in the rapidly growing field of membrane biophysics, with a focus on the structural properties of single lipid bilayers determined by different scattering techniques, namely neutron and X-ray scattering. The need for accurate lipid structural properties is emphasized by the sometimes conflicting results found in the literature, even in the case of the most studied lipid bilayers. Increasingly, accurate and detailed structural models require more experimental data, such as those from contrast varied neutron scattering and X-ray scattering experiments that are jointly refined with molecular dynamics simulations. This experimental and computational approach produces robust bilayer structural parameters that enable insights, for example, into the interplay between collective membrane properties and its components (e.g., hydrocarbon chain length and unsaturation, and lipid headgroup composition). From model studies such as these, one is better able to appreciate how a real biological membrane can be tuned by balancing the contributions from the lipid’s different moieties (e.g., acyl chains, headgroups, backbones, etc.).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1988

High-speed data acquisition for the Princeton University Dynamic Model Track

Real time analysis of data can reduce the time involved in exploring dynamic systems. The failure of the data acquisition system at the Princeton Dynamic Model Track prompted its replacement with a real time data acquisition system. Data can be obtained from an experiment and analyzed during and immediately following a data run. The new system employs high speed analog to digital conversion and a small computer to collect data. Sampling rates of 1000 hertz over 44 channels (44,000 words/sec) are obtainable. The data can be accessed as it enters the computer's environment where it may be displayed or stored for later processing. The system was tested on a helicopter rotor steep descent experiment. The data collected compares with previous data from a similar experiment.

Demko, Michael G.↗

NASA NTRS1993

An information model for use in software management estimation and prediction

This paper describes the use of cluster analysis for determining the information model within collected software engineering development data at the NASA/GSFC Software Engineering Laboratory. We describe the Software Management Environment tool that allows managers to predict development attributes during early phases of a software project and the modifications we propose to allow it to develop dynamic models for better predictions of these attributes.

Li, Ningda R.↗

NASA NTRS2003

A Systematic Global Mapping of the Radiation Field at Aviation Altitudes

This paper presents early results from aircraft measurements made by a Low-LET Radiation Spectrometer (LoLRS), as part of a long-range effort to study the complex dynamics of the atmospheric radiation field. For this purpose, a comprehensive data base is being generated to enable a multivariable global mapping (and eventually modeling) of doses and Linear-Energy-Transfer (LET) spectra at aviation altitudes. To accomplish this, a methodical collection of data from the LoLRS (and other instruments), is planned over extended periods of time, in a manner that complements some previous isolated and sporadic measurements by other workers, with the objective to generate a detailed long-range description of the cosmic-ray induced particle environment and to study its variability and dependence on atmospheric thickness, magnetic latitude, L-shell or rigidity, space weather, solar particle events, solar cycle effects, magnetic field variation, diurnal and seasonal effects, and atmospheric weather. Analysis of initial data indicates that the dose is rising with increasing altitude and increasing magnetic latitude. Comparison of total doses with predictions is in good agreement.

Stassinopoulos, E. G.↗

NASA NTRS2011

MODIS Aerosol Optical Depth Bias Adjustment Using Machine Learning Algorithms

To monitor the earth atmosphere and its surface changes, satellite based instruments collect continuous data. While some of the data is directly used, some others such as aerosol properties are indirectly retrieved from the observation data. While retrieved variables (RV) form very powerful products, they don't come without obstacles. Different satellite viewing geometries, calibration issues, dynamically changing atmospheric and earth surface conditions, together with complex interactions between observed entities and their environment affect them greatly. This results in random and systematic errors in the final products.

Albayrak, Arif↗

NASA NTRS2017

Orbit Determination Strategy and Simulation Performance for OSIRIS-REx Proximity Operations

The Origins Spectral Interpretation Resource Identification Security Regolith Explorer (OSIRISREx)is a NASA New Frontiers mission to the near-earth asteroid Bennu that will rendez vousin 2018, create a comprehensive and detailed set of observations over several years, collect a regolith sample, and return the sample to Earth in 2023. The Orbit Determination (OD) team isa sub-section of the Flight Dynamics System responsible for generating precise reconstructions and predictions of the spacecraft trajectory. The OD team processes radiometric data, LIDAR, as well as center-finding and landmark-based Optical Navigation images throughout the proximity operations phase to estimate and predict the spacecraft location within several meters. Stringent knowledge requirements stress the OD teams concept of operations and procedures to produce verified and consistent high quality solutions for observation planning, maneuver planning, and onboard sequencing. This paper will provide insight into the OD concept of operations and summarize the OD performance expected during the approach and early proximity operation phases,based on our pre-encounter knowledge of Bennu. Strategies and methods used to compare and evaluate predicted and reconstructed solutions are detailed. The use of high fidelity operational tests during early 2017 will stress the teams concept of operations and ability to produce precise OD solutions with minimal turn-around delay.

Leonard, Jason M.↗

NASA NTRS2017

Monitoring Land Surface Albedo and Vegetation Dynamics Using High Spatial and Temporal Resolution Synthetic Time Series from Landsat and the MODIS BRDF/NBAR/Albedo Product

Seasonal vegetation phenology can significantly alter surface albedo which in turn affects the global energy balance and the albedo warmingcooling feedbacks that impact climate change. To monitor and quantify the surface dynamics of heterogeneous landscapes, high temporal and spatial resolution synthetic time series of albedo and the enhanced vegetation index (EVI) were generated from the 500-meter Moderate Resolution Imaging Spectroradiometer (MODIS) operational Collection V006 daily BRDF (Bidirectional Reflectance Distribution Function) / NBAR (Nadir BRDF-Adjusted Reflectance) / albedo products and 30-meter Landsat 5 albedo and near-nadir reflectance data through the use of the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The traditional Landsat Albedo (Shuai et al., 2011) makes use of the MODIS BRDFAlbedo products (MCD43) by assigning appropriate BRDFs from coincident MODIS products to each Landsat image to generate a 30-meter Landsat albedo product for that acquisition date. The available cloud free Landsat 5 albedos (due to clouds, generated every 16 days at best) were used in conjunction with the daily MODIS albedos to determine the appropriate 30-meter albedos for the intervening daily time steps in this study. These enhanced daily 30-meter spatial resolution synthetic time series were then used to track albedo and vegetation phenology dynamics over three Ameriflux tower sites (Harvard Forest in 2007, Santa Rita in 2011 and Walker Branch in 2005). These Ameriflux sites were chosen as they are all quite nearby new towers coming on line for the National Ecological Observatory Network (NEON), and thus represent locations which will be served by spatially paired albedo measures in the near future. The availability of data from the NEON towers will greatly expand the sources of tower albedometer data available for evaluation of satellite products. At these three Ameriflux tower sites the synthetic time series of broadband shortwave albedos were evaluated using the tower albedo measurements with a Root Mean Square Error (RMSE) less than 0.013 and a bias within the range of 0.006. These synthetic time series provide much greater spatial detail than the 500 meter gridded MODIS data, especially over more heterogeneous surfaces, which improves the efforts to characterize and monitor the spatial variation across species and communities. The mean of the difference between maximum and minimum synthetic time series of albedo within the MODIS pixels over a subset of satellite data of Harvard Forest (16 kilometers by 14 kilometers) was as high as 0.2 during the snow-covered period and reduced to around 0.1 during the snow-free period. Similarly, we have used STARFM to also couple MODIS Nadir BRDF-Adjusted Reflectances (NBAR) values with Landsat 5 reflectances to generate daily synthetic times series of NBAR and thus Enhanced Vegetation Index (NBAR-EVI) at a 30-meter resolution. While normally STARFM is used with directional reflectances, the use of the view angle corrected daily MODIS NBAR values will provide more consistent time series. These synthetic times series of EVI are shown to capture seasonal vegetation dynamics with finer spatial and temporal details, especially over heterogeneous land surfaces.

Vegetation Index↗

NASA NTRS2017

Inflight Performance of the SDO Fine Pointing Science Mode

The Solar Dynamics Observatory (SDO) was successfully launched and deployed from its Atlas V launch vehicle on February 11, 2010. Three months later, on May 14, 2010, the fully commissioned heliophysics laboratory was handed over to Space Systems Mission Operations to begin its science mission. SDO is an Explorer-class mission now operating in a geosynchronous orbit, sending data 24 hours per day to a dedicated ground station in White Sands, New Mexico. It carries a suite of instruments designed to observe the Sun in multiple wavelengths at unprecedented resolution. The Atmospheric Imaging Assembly (AIA) includes four telescopes with 4096x4096 focal plane CCDs that can image the full solar disk in seven extreme ultraviolet and three ultraviolet-visible wavelengths. The Extreme Ultraviolet Variability Experiment (EVE) collects time-correlated data on the activity of the Sun's corona. The Helioseismic and Magnetic Imager (HMI) enables study of pressure waves moving through the body of the Sun.

Control↗

DOE OSTI2020

Kilometer-Scale Hydrologic Exchange Flows in a Gravel Bed River Corridor and Their Implications to Solute Migration

In this work, a well-characterized field site along a major river corridor was used to investigate the dynamic pathways and impacts of subsurface hydrogeologic structures on kilometer-scale hydrologic exchange flows between river water and groundwater. A aqueous uranium (U aq ) plume exists at the site that discharges to the Columbia River. We performed temporally intensive monitoring of specific conductance (SpC) and U aq concentrations within the plume at increasing distances from the river shoreline and at locations both within and outside a presumed subsurface pathway of lateral hydrologic exchange. Specific conductance and U aq were utilized as in-situ tracers of hydrologic exchange and associated groundwater-surface water mixing. Monitoring data was collected for an approximate two year period during which seasonal river elevations varied by more than 2 m causing distinct events of river water intrusion and retreat from the near-shore aquifer, and large temporal variations in well-water analyte concentrations. Statistical clustering analysis was applied to identify well groupings exhibiting common time series behaviors. PFLOTRAN was implemented within a modeling domain of 3.3 x 1.9 km with dynamic flow boundary conditions and accurate subsurface topography for the basal aquitard underlying the aquifer. In-silico calculations of river water tracer behavior were performed to assess predominant flow directions and velocities influenced by river stage variations and variable aquitard topography, and their collective influences on SpC and U aq concentrations. We demonstrate complicated multidirectional flow behaviors at the kilometer scale resulting from hydrologic exchange and subsurface structure that influence plume dynamics and determine the overall system behavior.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Climate sensitive size-dependent survival in tropical trees

Survival rates of large trees determine forest biomass dynamics. Survival rates of small trees are linked to mechanisms that maintain biodiversity across tropical forests. How species survival rates change with size offers insight into the links between biodiversity and ecosystem function across tropical forests. We tested patterns of size-dependent tree survival across the tropics using data from 1781 species and over two million individuals to assess whether tropical forests can be characterized by size-dependent life-history survival strategies. We found species were classifiable into four “survival modes” that explain life-history variation that shapes carbon-cycling and the relative abundances within forests. Frequently collected functional traits, such as wood density, leaf mass per area, and seed mass, were not generally predictive of species’ survival modes. Mean annual temperature and cumulative water deficit predicted the proportion of biomass of survival modes, indicating important links between evolutionary strategies, climate, and carbon cycling. The application of survival modes in demographic simulations predicted biomass change across forest sites. Our results reveal globally identifiable size-dependent survival strategies that differ across diverse systems in a consistent way. The abundance of survival modes and interaction with climate ultimately determine forest structure, carbon storage in biomass, and future forest trajectories.

Johnson, Daniel J.↗

NASA NTRS1995

Methodologies to determine forces on bones and muscles of body segments during exercise, employing compact sensors suitable for use in crowded space vehicles

Work under this grant was carried out by the author and by a graduate research assistant. An instrumented bicycle ergometer was implemented focusing on the stated objective: to estimate the forces exerted by each muscle of the feet, calf, and thigh of an individual while bicycling. The sensors used were light and compact. These were probes to measure muscle EMG activity, miniature accelerometers, miniature load sensors, and small encoders to measure angular positions of the pedal. A methodology was developed and implemented to completely describe the kinematics of the limbs using data from the sensors. This work has been published as a Master's Thesis by the Graduate student supported by the grant. The instrumented ergometer along with the sensors and instrumentation were tested during a KC-135 Zero-Gravity flight in July, 1994. A complete description of the system and the tests performed have been published as a report submitted to NASA Johnson Space Center. The data collected during the KC-135 flight is currently being processed so that a kinematic description of the bicycling experiment will be soon determined. A methodology to estimate the muscle forces has been formulated based on previous work. The methodology involves the use of optimization concepts so that the individual muscle forces that represent variables in dynamic equations of motion may be estimated. Optimization of a criteria (goal) function such as minimization of energy will be used along with constraint equations defined by rigid body equations of motion. Use of optimization principles is necessary, because the equations of motion alone constitute an indeterminate system of equations with respect to the large amount of muscle forces which constitute the variables in these equations. The number of variables is reduced somewhat by using forces measured by the load cells installed on the pedal. These load cells measure pressure and shear forces on the foot. The author and his collaborators at NASA and at the University of Alabama, Tuscaloosa, are continuing the work of reducing the experimental data from the KC-135 flight, and the implementation of the optimization methods to estimate muscle forces. As soon as results from these efforts are available, they will be published in reputable journals. Results of this work will impact studies addressing bone density loss and development of countermeasures to minimize bone loss in zero gravity conditions. By analyzing muscle forces on Earth and in Space during exercise, scientists could eventually formulate new exercises and machines to help maintain bone density. On Earth, this work will impact studies concerning arthritis, and will provide the means to study possible exercise countermeasures to minimize arthritis problems.

Figueroa, Fernando↗

DOE OSTI2024

Electronic interactions in Dirac fluids visualized by nano-terahertz spacetime interference of electron-photon quasiparticles

Ultraclean graphene at charge neutrality hosts a quantum critical Dirac fluid of interacting electrons and holes. Interactions profoundly affect the charge dynamics of graphene, which is encoded in the properties of its electron-photon collective modes: surface plasmon polaritons (SPPs). Here, we show that polaritonic interference patterns are particularly well suited to unveil the interactions in Dirac fluids by tracking polaritonic interference in time at temporal scales commensurate with the electronic scattering. Spacetime SPP interference patterns recorded in terahertz (THz) frequency range provided unobstructed readouts of the group velocity and lifetime of polariton that can be directly mapped onto the electronic spectral weight and the relaxation rate. Our data uncovered prominent departures of the electron dynamics from the predictions of the conventional Fermi-liquid theory. The deviations are particularly strong when the densities of electrons and holes are approximately equal. The proposed spacetime imaging methodology can be broadly applied to probe the electrodynamics of quantum materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2019

Multi-Formalism Modeling for Disaster Resilience, Forecasting, and Response

Disaster response preparedness, risk mitigation, and effective post-event response are critical for national security, public health, and economic integrity. For areas impacted by extreme events, timely and accurate impact assessments of lifeline infrastructures are critical. Traditional disaster management and situational awareness platforms are not capable of handling the massive influx of available information from traditional and non-traditional sources. Next-generation analytic assessment approaches, where use of dynamically sourced, heterogeneous data with near real-time availability are integrated into a continually-updated analytics framework. The objective of this research was to develop a foundational multi-formalism modeling platform to integrate novel data sources for use in improving situational awareness after a disaster event. Specifically, to 1) provide an architecture for machine-learning based data fusion various multi-sensor datasets; 2) classify, geolocate, and temporally bin novel data sources for use in spatially-enabled machine-learning based data fusion; and 3) derive confidence-based observation data with machine-learning based prediction to fill spatial and temporal observation gaps. This project developed a reusable cloud-based pipeline architecture for ingesting, storing, and analyzing relevant data, executing a range of deep-learning models, and visualize the information geographically. The pipeline development was informed by previous PNNL investments (StreamKit) and built upon leading-edge technologies consisting of: 1) a raw datastore; 2) a data enrichment pipeline with custom and third-party analytics; 3) enriched data storage; and 4) a custom UI to display the enriched data. Machine learning/deep learning models were selected and tested to classify social media images for relevancy and presence of flood waters. Our custom trained relevance models were run on a validation dataset and the overall accuracy of the relevance classifiers ranged from 65% to 90%. We found that convolutional neural network (CNN) models were both faster and more accurate than more traditional machine learning models. We applied the relevance classifier to remove non-relevant images and then undertook a limited training of CNN models to detect flood on ~1,800 labeled images. The testing accuracy of the flood detection models were ~88%. To improve the geographic location of both social media and oblique aerial images we tested a multi-step workflow built on a pre-trained deep-learning model. To identify similarity features from test and reference scenes, we applied the Xception and Faiss models to a set of test images with inaccurate spatial locations, as well and precisely-located reference scenes from the general area. Matching reference scenes to the test scenes with the closest similarity index had limited success in improving the precision of test scene location and additional methods have been identified for testing. Data fusion between authoritative geographic data (satellite imaging) and non-authoritative crowdsourced data is an underlying theme throughout this research and methods are tested bring these data to a common geographic form through feature matching and transformations. The automated extraction of supporting information for disaster response from oblique aerial images or ground-level social media images is accomplished through semantic image segmentation and transformation. The non-authoritative data are used to validate the authoritative data collections and processing and optionally, t+0 flood models. In addition, the common form geographic data are combined with other static landscape features to develop spatially-explicit training vectors used in deep generative statistical inference models to develop complex non-linear relationships in static and dynamic data and ultimately derive a spatially continuous and probabilistic assessment of flood damage with the event domain.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2017

Simulation of Swap-Out Reliability For The Advance Photon Source Upgrade

The proposed upgrade of the Advanced Photon Source (APS) to a multibend-achromat lattice relies on the use of swap-out injection to accommodate the small dynamic acceptance, allow use of unusual insertion devices, and minimize collective effects at high single-bunch charge. This, combined with the short beam lifetime, will make injector reliability even more important than it is for top-up operation. We used historical data for the APS injector complex to obtain probability distributions for injector up-time and down-time durations. Using these distributions, we simulated several years of swap-out operation for the upgraded lattice for several operatingmodes. The results indicate that obtaining very high availability of beam in the storage ring will require improvements to injector reliability.

Borland, M.↗

DOE OSTI2013

Dynamic Line Rating Oncor Electric Delivery Smart Grid Program

Electric transmission lines are the lifeline of the electric utility industry, delivering its product from source to consumer. This critical infrastructure is often constrained such that there is inadequate capacity on existing transmission lines to efficiently deliver the power to meet demand in certain areas or to transport energy from high-generation areas to high-consumption regions. When this happens, the cost of the energy rises; more costly sources of power are used to meet the demand or the system operates less reliably. These economic impacts are known as congestion, and they can amount to substantial dollars for any time frame of reference: hour, day or year. There are several solutions to the transmission constraint problem, including: construction of new generation, construction of new transmission facilities, rebuilding and reconductoring of existing transmission assets, and Dynamic Line Rating (DLR). All of these options except DLR are capital intensive, have long lead times and often experience strong public and regulatory opposition. The Smart Grid Demonstration Program (SGDP) project co-funded by the Department of Energy (DOE) and Oncor Electric Delivery Company developed and deployed the most extensive and advanced DLR installation to demonstrate that DLR technology is capable of resolving many transmission capacity constraint problems with a system that is reliable, safe and very cost competitive. The SGDP DLR deployment is the first application of DLR technology to feed transmission line real-time dynamic ratings directly into the system operation’s State Estimator and load dispatch program, which optimizes the matching of generation with load demand on a security, reliability and economic basis. The integrated Dynamic Line Rating (iDLR)1 collects transmission line parameters at remote locations on the lines, calculates the real-time line rating based on the equivalent conductor temperature, ambient temperature and influence of wind and solar radiation on the stringing section, transmits the data to the Transmission Energy Management System, validates its integrity and passes it on to Oncor and ERCOT (Electric Reliability Council of Texas) respective system operations. The iDLR system is automatic and transparent to ERCOT System Operations, i.e., it operates in parallel with all other system status telemetry collected through Supervisory Control and Data Acquisition (SCADA) employed across the company.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2015

Optimizing accuracy of determinations of CO₂ storage capacity and permanence, and designing more efficient storage operations: An example from the Rock Springs Uplift, Wyoming

At a potential injection site on the Rock Springs Uplift in southwest Wyoming, an investigation of confining layers was undertaken to develop and test methodology, identify key data requirements, assess previous injection scenarios relative to detailed confining layer properties, and integrate all findings in order to reduce the uncertainty of CO₂ storage permanence. The assurance of safe and permanent storage of CO₂ at a storage site involves a detailed evaluation of the confining layers. Four suites of field data were recognized as crucial for determining storage permanence relative to the confining layers; seismic, core and petrophysical data from a wellbore, formation fluid samples, and in-situ formation tests. Core and petrophysical data were used to create a vertical heterogenic property model that defined porosity, permeability, displacement pressure, geomechanical strengths, and diagenetic history. These analyses identified four primary confining layers and multiple redundant confining layers. In-situ formation tests were used to evaluate fracture gradients, regional stress fields, baseline microseismic data, step-rate injection tests, and formation perforation responses. Seismic attributes, correlated with the vertical heterogenic property models, were calculated and used to create a 3-D volume model over the entire site. The seismic data provided the vehicle to transform the vertical heterogenic property model into a horizontal heterogenic property model, which allowed for the evaluation of confining layers across the entire study site without risking additional wellbore perforations. Lastly, formation fluids were collected and analyzed for geochemical and isotopic compositions from stacked reservoir systems. These data further tested primary confining layers, by evaluating the evidence of mixing between target reservoirs (mixing would imply an existing breach of primary confining layers). All data were propagated into a dynamic, heterogenic geologic property model used to test various injection scenarios. These tests showed that the study site could retain 25MT of injected CO₂ over an injection lifespan of 50 years. Major findings indicate that active reservoir pressure management through reservoir fluid production (minimum of three production wells) greatly reduces the risk of breaching a confining layer. To address brine production, a well completion and engineering study was incorporated to reduce the risks of scaling and erosion during injection and production. These scenarios suggest that the dolostone within the Mississippian Madison Limestone is the site’s best injection/production target by two orders of magnitude, and that commercial well equipment would meet all performance requirements. This confirms that there are multiple confining layers in southwest Wyoming that are capable of retaining commercial volumes of CO₃, making Wyoming’s Paleozoic reservoirs ideal storage targets for low-risk injection and long-term storage. This study also indicates that column height retention calculations are reduced in a CO₂-brine system relative to a hydrocarbon-brine system, which is an observation that affects all potential CCS sites. Likewise, this study identified the impacts that downhole testing imparts on reservoir fluids, and the likelihood of introducing uncertainty in baseline site assumptions and later modeling.

25 ENERGY STORAGE↗