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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Assessing chromophoric dissolved organic matter (CDOM) distribution, stocks, and fluxes in Apalachicola Bay using combined field, VIIRS ocean color, and model observations

Understanding the role of estuarine-carbon fluxes is essential to improve estimates of the global carbon budget. Dissolved organic matter (DOM) plays an important role in aquatic carbon cycling. Here, the chromophoric fraction of DOM (CDOM) can be readily detected via in situ and remotely-sensed optical measurements. DOM properties, including CDOM absorption coefficient at 412 nm (a g 412) and dissolved organic carbon (DOC) concentrations were examined in Apalachicola Bay, a national estuarine research reserve located in the northeast Gulf of Mexico, using in situ and satellite observations during the spring and fall of 2015. Synoptic and accurate representation of estuarine-scale processes using satellite ocean color imagery necessitates the removal of atmospheric contribu- tion (~90%) to signals received by satellite sensors to successfully link to in situ observations. Three atmospheric correction schemes (e.g., Standard NIR correction, Iterative NIR correction, and SWIR correction) were tested first to find a suitable correction scheme for the VIIRS imagery in low to moderately turbid Apalachicola Bay. The iterative NIR correction performed well, and validation showed high correlation (R 2 = 0.95, N = 25) against in situ light measurements. A VIIRS-based CDOM algorithm was developed (R 2 = 0.87, N = 9) and validated (R 2 = 0.76, N = 20, RMSE = 0.29 m -1 ) against in situ observations. Subsequently, ag412 was used as a proxy of DOC in March (DOC = 1.08 + 0.94 × a g 412,R 2 =0.88, N = 13)and in November (DOC= 1.61 + 1.33 × a g 412, R 2 = 0.83, N = 24) to derive DOC maps that provided synoptic views of DOC distribution, sources, and their transport to the coastal waters during the wet and dry seasons. The estimated DOC stocks were ~3.71 × 10 6 kg C in March and ~4.07 × 10 6 kg C in November over an area of ~560 km2. Volume flux (out of the bay) almost doubled for March 24 (735 m 3 s -1 ) relative to November 4 (378 m 3 s -1 ). However, estimates of DOC fluxes exported out of the bay from model-derived currents and satellite-derived DOC were only marginally greater in March (0.163 × 10 6 kg C d -1 ) than in November (0.124 × 10 6 kg C d -1 ) and reflected greater DOC stocks in the fall. Finally, the combination of satellite-, field-, and model-based observations revealed the strong linkage between the Apalachicola River plume, a major source of DOM, and the overall hydrodynamic forcing that controlled distributions of CDOM abundance, DOC concentration, stocks, and fluxes in the bay.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Carbon and Water Fluxes of the Boreal Evergreen Needleleaf Forest Biome Constrained by Assimilating Ecosystem Carbonyl Sulfide Flux Observations

Gross primary production (GPP) by boreal forests is highly sensitive to environmental changes. However, GPP simulated by land surface models (LSMs) remains highly uncertain due to the lack of direct photosynthesis observations at large scales. Carbonyl sulfide (COS) has emerged as a promising proxy to improve the representation of GPP in LSMs. Because COS is absorbed by vegetation following the same diffusion pathway as CO2 during photosynthesis and not emitted back to the atmosphere, incorporating a mechanistic representation of vegetation COS uptake in LSMs allows using COS observations to refine GPP representation. Here, we perform ecosystem COS flux and GPP data assimilations to constrain the COS- and GPP-related parameters in the ORCHIDEE LSM for boreal evergreen needleleaf forests (BorENF). Assimilating ecosystem COS fluxes at Hyytiälä forest increases the simulated net ecosystem COS uptake by 14%. This increase largely results from changes in the internal conductance to COS, highlighting the need to improve the representation of COS internal diffusion and consumption. Moreover, joint assimilation of ecosystem COS flux and GPP at Hyytiälä improves the simulated latent heat flux, contrary to the GPP-only data assimilation, which fails to do so. Finally, we scaled this assimilation framework up to the boreal region and find that the joint assimilation of COS at Hyytiälä and GPP fluxes at 10 BorENF sites increases the modeled vegetation COS uptake up to 18%, but not GPP. Therefore, this study encourages the use of COS flux observations to inform GPP and latent heat flux representations in LSMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

A Bayesian, multivariate calibration for Globigerinoides ruberMg/Ca

The use of Mg/Ca in marine carbonates as a paleothermometer has been challenged by observations that implicate salinity as a contributing influence on Mg incorporation into biotic calcite and that dissolution at the sea-floor alters the original Mg/Ca. Yet, these factors have not yet been incorporated into a single calibration model. In this paper, we introduce a new Bayesian calibration for Globigerinoides ruber Mg/Ca based on 186 globally distributed core top samples, which explicitly takes into account the effect of temperature, salinity, and dissolution on this proxy. Our reported temperature, salinity, and dissolution (here expressed as deep-water ΔCO 2- 3 ) sensitivities are (±2σ) 8.7±0.9%/°C, 3.9±1.2%/psu, and 3.3±1.3%/μmol.kg -1 below a critical threshold of 21 μmol/kg in good agreement with previous culturing and core-top studies. We then perform a sensitivity experiment on a published record from the western tropical Pacific to investigate the bias introduced by these secondary influences on the interpretation of past temperature variability. This experiment highlights the potential for misinterpretations of past oceanographic changes when the secondary influences of salinity and dissolution are not accounted for. Finally, multiproxy approaches could potentially help deconvolve the contributing influences but this awaits better characterization of the spatio-temporal relationship between salinity and δ 18 O sw over millennial and orbital timescales.

58 GEOSCIENCES↗
DOE OSTI2020

A mass spectrometry-based high-throughput screening method for engineering fatty acid synthases with improved production of medium-chain fatty acids

Microbial cell factories have been extensively engineered to produce free fatty acids (FFAs), key components of crucial nutrients, soaps, industrial chemicals, and fuels. Yet, our ability to control the specificity of microbially synthesized FFAs is still limited for medium-chain fatty acids (MCFAs), which is mainly due to lack of high-throughput approaches for FFA analysis. Here we report a mass spectrometry (MS)-based method for rapid profiling of MCFAs in Saccharomyces cerevisiae by using membrane lipids as a proxy. Specially, matrix-assisted laser desorption/ionization time-of-flight (MALDI-ToF) MS was used to detect shorter acyl chain phosphatidylcholines from membrane lipids and the m/z peak ratio at 730 and 758 was used as an index for improved MCFA production. This yeast colony-based method can be performed at a rate of ~2 second per sample, which is much faster than the gold standard gas chromatography (GC)-MS (typically > 30 min per sample). To demonstrate the power of this method, we performed site-saturation mutagenesis of the yeast fatty acid synthase and identified 9 mutants that resulted in improved MCFA production relative to the wild-type strain. This colony-based MALDI-ToF MS screening method offers an alternative way for engineering microbial fatty acid compositions in a high-throughput manner.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

PredCom: A Predictive Approach to Collecting Approximated Communication Traces

Communication traces collected from MPI applications are an important source of information for performance optimization as they can help analysts determine communication patterns and identify inefficiencies. However, their collection, especially at scale, is time consuming, since it usually requires running the complete target application on a large number of nodes. In this work, we present PredCom, a tool-chain to generate a predictive communication proxy based on information gathered from a few small scale runs, which allows us to extract approximate communication traces with an accuracy high enough for most analysis goals. For this, we combine LLVM passes on the original source code (to capture static program structure) with parameter prediction (to capture dynamic and scaling behavior). This approach drastically reduces the time needed for collecting the communication traces, even for traces on large numbers of MPI processes. Here, we demonstrate that PredCom generates communication traces of various applications up to 1612x faster with an accuracy loss of 0.11 on average compared to the original large-scale traces, and we show that the generated traces can be used to optimize process placement.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Machine Learning for Solar Accessibility: Implications for Low-Income Solar Expansion and Profitability

The solar industry in the US typically uses a credit score such as the FICO score as an indicator of consumer utility payment performance and credit worthiness to approve customers for new solar installations. Using data on over 800,000 utility payment performance and over 5,000 demographic variables, we compare machine learning and econometric models to predict the probability of default to credit-score cutoffs. Here, we compare these models across a variety of measures, including how they affect consumers of different socio-economic backgrounds and profitability. We find that a traditional regression analysis using a small number of variables specific to utility repayment performance greatly increases accuracy and LMI inclusivity relative to FICO score, and that using machine learning techniques further enhances model performance. Relative to FICO, the machine learning model increases the number of low-to-moderate income consumers approved for community solar by 1.1% to 4.2% depending on the stringency used for evaluating potential customers, while decreasing the default rate by 1.4 to 1.9 percentage points. Using electricity utility repayment as a proxy for solar installation repayment, shifting from a FICO score cutoff to the machine learning model increases profits by 34% to 1882% depending on the stringency used for evaluating potential customers.

Davuluri, Sruthi↗
DOE OSTI2016

Environmental Characterization Scans Using Sodium-Iodide Gamma Spectroscopy to Determine Ra-226 Concentration in Surface Soil - 16326

The recent natural gas drilling boom has brought heightened attention to the issue of TENORM waste management. Radioactive residues from the extraction, treatment, and purification of materials containing uranium and thorium, or their progeny, pose a public health hazard primarily in the form of ground water contamination. Hydraulic fracturing can liberate formation waters from shale; the high salinity of which can cause radium to move with the water to the surface. The disposal and handling of this water has been a subject of public controversy. For soil remediation projects, radium-226 is generally one of the radionuclides of concern due to its alpha emissions. However performing a survey scan for Ra-226 is difficult because its photon emissions are low energy and have minimal probability of emission per decay (186.2 keV, 3.3% abundance). Additionally resolving its emission from U-235 (185.7 keV, 57% abundance) with field instrumentation is not feasible. Therefore detection of Ra-226 relies on the presence and photon emissions of its progeny. This paper outlines methodology for calculating the minimum detectable concentration of Ra-226 in characterization scans using a sodium-iodide detector array coupled with a multi-channel analyzer. Using progeny ingrowth as a detection proxy requires knowing decay time in order to determine progeny emission rates. Knowledge of site history, process, and prior survey results can be key in determining an accurate decay time. However in the absence of such information in-situ measurements can be used to estimate decay time by comparing observed progeny emission ratios against established values. These values are determined using Oak Ridge National Laboratory's ORIGEN-ARP program to generate photon energy spectra for Ra-226 and its progeny at specified time intervals. Once decay time is established, the detector's minimum detectable concentration can be calculated by synthesizing the expected emission spectra and Los Alamos National Laboratory's Monte Carlo N-Particle transport code to simulate detector response. Traditionally, one of the steps in calculating a system's minimum detectable concentration is to place an appropriate button source under the detector, record the response, and then move the source by a specified increment. This process is repeated until the detector's spatial efficiency has been mapped. Due to the impracticality of performing this with Ra-226 button sources of varying decay times, Monte Carlo N-Particle transport code was utilized to determine the detector's spatial efficiency. A dime sized source with the established emission spectra was placed into the center of a rectangle of soil. This rectangle, including the source, was used as a lattice element to fill the region of space below the detector. The simulation was designed to track the energy distribution of pulses created in the sodium iodide crystal using the F8 tally, and was setup to isolate each source; thereby correlating detector response to source position. The simulation output required post-processing to determine conversion factors from counts per second to disintegrations per second. Post-processing accounted for subtraction of the Compton continuum, and average the data over the scanning pattern to obtain the expected spatial sensitivity during a characterization study. This was performed for the following photo-peaks: 609 keV (Bi-214), and 1764 keV (Bi-214). For calculation of the scan minimum detectable concentration, the surveyor efficiency coefficient from NUREG 1507 was supplanted in favor of a scan speed deviation factor. This was used because the detector utilizes computerized data collection (spatial and spectral). This report finds that, for two shielded 10.16 cm x 10.16 cm x 40.64 cm sodium-iodide crystals with integrated multi-channel analyzers, the minimum detectable concentration in surface soils is less than 1 pCi per gram Ra-226 for a 1 second survey measurement. These results assume a volumetric source with a maximum depth of 15.24 cm and a minimum decay time of 21 days. Verification of the system's capabilities was performed by sending soil samples from scanned areas, and locations where in-situ measurements were performed, to an independent laboratory for analysis. The laboratory results demonstrated that scanned area was principally comprised of soil containing less than 1 pCi per gram Ra-226. This borders the system MDC and it underestimated the concentration of radium in the soil. The probable causes of this is escape of radon gas from the surface soil. Radon is the daughter of radium, and has a high mobility in soil. If the gas escapes, then the progeny used as the detection proxy will be present in lower than expected concentrations. Radionuclide identification using high volume sodium-iodide crystals in low level characterization scans is demonstrated to be possible. Further system refinement will focus on developing a methodology to estimate a radon escape factor for site specific use. Accounting for radon escape in the top 15.24 cm of soil will allow the system to better estimate Ra-226 concentrations in soil. This is a significant advancement over the gross counting statistics used in most studies to satisfy MARSSIM survey requirements, because it will increase stakeholder confidence. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

An Asynchronous Many-Task Implementation of In-Situ Statistical Analysis using Legion.

In this report, we propose a framework for the design and implementation of in-situ analy- ses using an asynchronous many-task (AMT) model, using the Legion programming model together with the MiniAero mini-application as a surrogate for full-scale parallel scientific computing applications. The bulk of this work consists of converting the Learn/Derive/Assess model which we had initially developed for parallel statistical analysis using MPI [PTBM11], from a SPMD to an AMT model. In this goal, we propose an original use of the concept of Legion logical regions as a replacement for the parallel communication schemes used for the only operation of the statistics engines that require explicit communication. We then evaluate this proposed scheme in a shared memory environment, using the Legion port of MiniAero as a proxy for a full-scale scientific application, as a means to provide input data sets of variable size for the in-situ statistical analyses in an AMT context. We demonstrate in particular that the approach has merit, and warrants further investigation, in collaboration with ongoing efforts to improve the overall parallel performance of the Legion system.

Pebay, Philippe Pierre↗
DOE OSTI2019

Chlorophyll–carotenoid excitation energy transfer and charge transfer in Nannochloropsis oceanica for the regulation of photosynthesis

Nonphotochemical quenching (NPQ) is a proxy for photoprotective thermal dissipation processes that regulate photosynthetic light harvesting. The identification of NPQ mechanisms and their molecular or physiological triggering factors under in vivo conditions is a matter of controversy. In this work, to view chlorophyll (Chl)–zeaxanthin (Zea) excitation energy transfer (EET) and charge transfer (CT) as possible NPQ mechanisms, we performed transient absorption (TA) spectroscopy on live cells of the microalga Nannochloropsis oceanica . We gained evidence for the operation of both EET and CT quenching by observing spectral features associated with the Zea S 1 and Zea •+ excited-state absorption (ESA) signals, respectively, after Chl excitation. Knockout mutants for genes encoding either violaxanthin de-epoxidase or LHCX1 proteins exhibited strongly inhibited NPQ capabilities and lacked detectable Zea S 1 and Zea •+ ESA signals in vivo, which strongly suggests that the accumulation of Zea and active LHCX1 is essential for both EET and CT quenching in N. oceanica .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Improving tropical cyclone rapid intensification forecasts with satellite measurements of sea surface salinity and calibrated machine learning

Forecasting rapid intensification (RI) of tropical cyclones (TC) is a mission known for large errors. One under-researched factor that affects TC intensification is salinity, which is important for density stratification in certain ocean regions and can affect the surface enthalpy flux under a strengthening hurricane. To investigate the impact and efficacy of using salinity information in state-of-the-art forecasting, we use a statistical model consisting of a variety of machine learning (ML) methods. For salinity data, we use satellite measurements of pre-storm sea surface salinity (SSS) as a proxy for the salinity stratification. We train and test the model on various ocean basins, including the Atlantic, eastern North Pacific and western North Pacific. A calibrator is trained on top of the ML models to correct and enhance probability forecasts. The calibrator significantly improves probability forecasts relative to recent works. The ML model performance is improved with the addition of SSS in the Eastern North Pacific, western North Pacific, and the Caribbean subregion of the North Atlantic, and the overall model performance is better than previous studies. SSS decreases model skill for a model trained on the full Atlantic basin. In the Indian Ocean, SSS is also notably correlated with RI occurrence, but the TC samples are not sufficient to train ML models.

hurricane↗
DOE OSTI2015

NO TIME FOR DEAD TIME: TIMING ANALYSIS OF BRIGHT BLACK HOLE BINARIES WITH NuSTAR

Timing of high-count-rate sources with the NuSTAR Small Explorer Mission requires specialized analysis techniques. NuSTAR was primarily designed for spectroscopic observations of sources with relatively low count rates rather than for timing analysis of bright objects. The instrumental dead time per event is relatively long (∼2.5 msec) and varies event-to-event by a few percent. The most obvious effect is a distortion of the white noise level in the power density spectrum (PDS) that cannot be easily modeled with standard techniques due to the variable nature of the dead time. In this paper, we show that it is possible to exploit the presence of two completely independent focal planes and use the cospectrum, the real part of the cross PDS, to obtain a good proxy of the white-noise-subtracted PDS. Thereafter, one can use a Monte Carlo approach to estimate the remaining effects of dead time, namely, a frequency-dependent modulation of the variance and a frequency-independent drop of the sensitivity to variability. In this way, most of the standard timing analysis can be performed, albeit with a sacrifice in signal-to-noise ratio relative to what would be achieved using more standard techniques. We apply this technique to NuSTAR observations of the black hole binaries GX 339–4, Cyg X-1, and GRS 1915+105.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Impact of Ducting on Heat Pump Water Heater Space Conditioning Energy Use and Comfort

Increasing penetration of heat pump water heaters (HPWHs) in the residential sector will offer an important opportunity for energy savings, with a theoretical energy savings of up to 63% per water heater and up to 11% of residential energy use (EIA 2009). However, significant barriers must be overcome before this technology will reach widespread adoption in the Pacific Northwest region and nationwide. One significant barrier noted by the Northwest Energy Efficiency Alliance (NEEA) is the possible interaction with the homes’ space conditioning system for units installed in conditioned spaces. Such complex interactions may decrease the magnitude of whole-house savings available from HPWH installed in the conditioned space in cold climates and could lead to comfort concerns (Larson et al. 2011; Kresta 2012). Modeling studies indicate that the installation location of HPWHs can significantly impact their performance and the resultant whole-house energy savings (Larson et al. 2012; Maguire et al. 2013). However, field data are not currently available to validate these results. This field evaluation of two GE GeoSpring HPWHs in the PNNL Lab Homes is designed to measure the performance and impact on the Lab Home HVAC system of a GE GeoSpring HPWH configured with exhaust ducting compared to an unducted GeoSpring HPWH during heating and cooling season periods; and measure the performance and impact on the Lab Home HVAC system of the GeoSpring HPWH with both supply and exhaust air ducting as compared to an unducted GeoSpring HPWH during heating and cooling season periods. Important metrics evaluated in these experiments include water heater energy use, HVAC energy use, whole house energy use, interior temperatures (as a proxy for thermal comfort), and cost impacts. This technical report presents results from the PNNL Lab Homes experiment.

energy efficiency↗
DOE OSTI2025

Challenges in spatial metabolomics and proteomics for functional tissue unit and single-cell resolution

While transcriptomics is the most broadly applied technology for global spatial and single cell measurements in healthy and diseased tissues. Transcripts are often used as a proxy for protein and even metabolite measurements, but it has become commonly accepted that extrapolating this kind of information is a poor proxy and not a substitute for direct measurement. Within the last decade advanced developments of mass spectrometry-based assays have made these direct measurements not only possible, but routine. Where mass spectrometry has become an enabling technology, and various methods can now detect hundreds of metabolites and thousands of proteins from samples. Not only can this be performed within bulk measurements, but much effort has been directed into translating these measurements to single cells and tissues at cellular resolution. The information obtained from mass spectrometry is now able to trace metabolic events and decipher feedback loops across anatomical regions, connecting genetic and metabolic networks that define phenotypes. Herein, we will broadly overview developments in the field over the past decade, leading into several case studies which highlight the direct measurement of metabolites, proteins, and proteoforms from thinly sliced tissues. Much of this work is feasible due to multidisciplinary team science, and we offer brief perspective on paths forward and the challenges that persist with adoption and application of spatial omics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Measuring Thread Timing to Assess the Feasibility of Early-Bird Message Delivery Across Systems and Scales

Early-bird communication is a communication/computation overlap technique that leverages fine-grained communication to improve application run-time. Communication is divided such that each individual thread can initiate transmission of its portion of the data upon completion rather than waiting for a dedicated communication phase. The benefit of early-bird communication depends on the completion timing of the individual threads: On the one hand, if all threads are complete at nearly the same time, the overheads of sending multiple messages will accumulate, leading to performance that is worse than if a single message had been sent. On the other hand, if thread completions are spread out in time, those that complete earlier can send data while others continue working, leading to performance that is better than if a single message had been sent. The challenge is that the completion times are currently unknown and can vary based on application, problem size, system software, and underlying hardware. In this paper, we address this lacuna by measuring and evaluating the potential overlap afforded by early-bird communication for a selection of proxy applications. These measurements help us understand whether a given application could benefit from early-bird communication. Here, we present our technique for gathering this data and evaluate data collected from three proxy applications: MiniFE, MiniMD, and MiniQMC. Each application is run on three systems with distinct CPU architectures and strong scales across three run sizes. To characterize the behavior of these workloads, we study the trends of thread timings at both a macro level, across all threads across all runs of an application, and a micro level, that is, within a single process of a single run. We observe that our tested applications exhibit significantly different thread arrival distributions. The machine used had a significant impact, with the window of potential overlap varying by as much as an order of magnitude.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Proxy-Based Bayesian Inversion Of Poroelastic Simulations To Interpret Strain Tensor Data Measured During Well Testing

The long runtimes of 3D poroelastic numerical simulators makes it impractical to interpret deformation datasets using many inversion schemes. Recent advances in instrumentation have made it possible to measure the strain tensor during well testing, but the lack of robust inversion methods is limiting the ability to interpret these data. We have developed an inversion workflow that reduces the number of computations required to complete a Bayesian inversion using DREAMzs. The workflow trains a KNN model using output from the poroelastic simulator, and then uses the KNN model as a proxy for the simulator during inversion. The workflow also includes a strategy for ensuring the results from the proxy model converge to the results from the simulator, ensuring the accuracy of the final results. An idealized example configured to represent a well test in a deep aquifer is used to verify that the workflow correctly identifies parameters and characterizes noise. Field data measured using strainmeters during an injection test at an oil reservoir in Oklahoma are used to evaluate performance with a real dataset. The workflow identified 265 history matching solutions out of 1240 total simulation runs (21% acceptance ratio), and the results are used to characterize posterior parameter distribution and evaluate the prediction uncertainty. This approach makes it feasible to invert strain data measured during well testing and this has the potential to improve the characterization of aquifers and reservoirs.

Roudini, Soheil↗
DOE OSTI2015

Intermittent Renewable Management Pilot Phase 2

The Intermittent Renewable Management Pilot - Phase 2 (IRM2) was designed to study the feasibility of demand-side resources to participate into the California Independent System Operator (CAISO) wholesale market as proxy demand resources (PDR). The pilot study focused on understanding the issues related with direct participation of third-parties and customers including customer acceptance; market transformation challenges (wholesale market, technology); technical and operational feasibility; and value to the rate payers, DR resource owners and the utility on providing an enabling mechanism for DR resources into the wholesale markets. The customer had the option of committing to either three contiguous hour blocks for 24 days or six contiguous hours for 12 days a month with day-ahead notification that aligned with the CAISO integrated forward market. As a result of their being available, the customer was paid $10/ kilowatt (kW)-month for capacity in addition to CAISO energy settlements. The participants were limited to no more than a 2 megawatt (MW) capacity with a six-month commitment. Four participants successfully engaged in the pilot. In this report, we provide the description of the pilot, participant performance results, costs and value to participants as well as outline some of the issues encountered through the pilot. Results show that participants chose to participate with storage and the value of CAISO settlements were significantly lower than the capacity payments provided by the utility as incentive payments. In addition, this pilot revealed issues both on the participant side and system operations side. These issues are summarized in the report.The Intermittent Renewable Management Pilot - Phase 2 (IRM2) was designed to study the feasibility of demand-side resources to participate into the California Independent System Operator (CAISO) wholesale market as proxy demand resources (PDR). The pilot study focused on understanding the issues related with direct participation of third-parties and customers including customer acceptance; market transformation challenges (wholesale market, technology); technical and operational feasibility; and value to the rate payers, DR resource owners and the utility on providing an enabling mechanism for DR resources into the wholesale markets.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

Managing well leakage risks at a geologic carbon storage site with many wells

Potential geologic carbon storage (GCS) sites with a history of oil and gas production have well-characterized injectivity and storage capacity, but the presence of legacy wells increases leakage risk. Thus, the success of GCS operations at sites with many wells will require a thorough leakage risk assessment and a robust strategy for managing well leakage risk over the lifetime of the project. In this study, we demonstrate a workflow that uses the National Risk Assessment Partnership’s open-source Integrated Assessment Model to quantify well leakage risks and test the performance of various leakage risk management strategies at a heavily drilled GCS site. Here, our model simulates a 50-year basin-scale injection of CO 2 at a hypothetical site based on the Kimberlina Project Site in the Southern San Joaquin Valley of California. Brine and CO 2 leakage through 1,000 legacy wells into a USDW are stochastically simulated as a proxy for risk. We consider multiple scenarios that explore the efficacy of various well leakage risk management strategies with changes in well leakage behavior, reservoir behavior, and post-injection site care (PISC) length. Predicted leakage at the site after 100 years was small with a maximum CO 2 leakage of 102.1 tonnes (4.08×10 -5 % of the 250 Mt injected) and a maximum brine leakage of 2.4 tonnes. Leakage risk management strategies based on accurate prior information about well leak probability reduced leakage risks more effectively at the modeled site and were more robust with respect to reservoir uncertainty than strategies based on the distance of the legacy well from the injector. The importance of the PISC period length was not clear as it had a negligible impact on CO 2 leakage risk but a sizable impact on brine leakage risk in our model.

58 GEOSCIENCES↗