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At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

April 2020 Darshan counters from the Summit supercomputer

This dataset is the Darshan counters collected from the Summit supercomputer in a month of April 2020. 1. Description of methods used for collection/generation of data: Job submitted on Summit HPC system when completed successfully and has made I/O calls (captured by Darshan tool) writes a Darshan log file on alpine filesystem. One job can have multiple `jsrun` commands and Darshan will generate separate logs each log corresponding to an `jsrun` command, so a job can have one or more Darshan logs associated with it. 2. Methods for processing the data: To process the data, we first use `darshan-util` tool to parse the Darshan logs. Then we restructure the logs and merge data from multiple Darshan logs if they belong to the same Summit job.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Performance Analysis Tool for HPC and Big Data Applications on Scientific Clusters

Big data is prevalent in HPC computing. Many HPC projects rely on complex workflows to analyze terabytes or petabytes of data. These workflows often require running over thousands of CPU cores and performing simultaneous data accesses, data movements, and computation. It is challenging to analyze the performance involving terabytes or petabytes of workflow data or measurement data of the executions, from complex workflows over a large number of nodes and multiple parallel task executions. To help identify performance bottlenecks or debug the performance issues in large-scale scientific applications and scientific clusters, we have developed a performance analysis framework, using state-ofthe- art open-source big data processing tools. Our tool can ingest system logs and application performance measurements to extract key performance features, and apply the most sophisticated statistical tools and data mining methods on the performance data. It utilizes an efficient data processing engine to allow users to interactively analyze a large amount of different types of logs and measurements. To illustrate the functionality of the big data analysis framework, we conduct case studies on the workflows from an astronomy project known as the Palomar Transient Factory (PTF) and the job logs from the genome analysis scientific cluster. Our study processed many terabytes of system logs and application performance measurements collected on the HPC systems at NERSC. The implementation of our tool is generic enough to be used for analyzing the performance of other HPC systems and Big Data workows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Neural network modeling of in situ fluid-filled pore size distributions in subsurface shale reservoirs under data constraints

Subsurface nuclear magnetic resonance (NMR) logs acquired in the wellbore environment are sensitive to fluid-filled pore size distribution, fluid mobility, permeability, and porosity in the near-wellbore reservoir volume. NMR response of a formation layer is processed to extract the T2 distribution, which approximates the fluid-filled pore size distribution. NMR logs are acquired in limited number of wells due to financial and operational challenges, which adversely affects reservoir characterization. We developed here two neural-network-based machine learning techniques, long short-term memory (LSTM) network and variational autoencoder with a convolutional layer (VAEc) network, to process the ‘easy-to-acquire’ formation mineral and fluid saturation logs to generate synthetic NMR T2 distributions in the absence of ‘hard-to-acquire’ NMR T2 distribution log. Both the predictive models are trained and tested on limited wireline log measurements randomly selected from a 300-ft depth interval of the Bakken shale formation. Synthesis performances of LSTM and VAEc models in terms of R 2 are 0.78 and 0.75, respectively. Noise is inevitable in logging data due to the complex wellbore and formation conditions. Notably, both the predictive models robustly synthesize the fluid-filled pore size distributions in the presence of 50% noise in input logs and 30% noise in training T2 data. The performance of the proposed methodology improves with access to larger volume of training data from other formation types. The proposed method is critical to the synthesis of in situ fluid-filled pore size distributions in shale formations under data constraints due to financial and operational challenges.

58 GEOSCIENCES↗
DOE OSTI2015

Linearly supporting feature extraction for automated estimation of stellar atmospheric parameters

We describe a scheme to extract linearly supporting (LSU) features from stellar spectra to automatically estimate the atmospheric parameters T{sub eff}, log g, and [Fe/H]. “Linearly supporting” means that the atmospheric parameters can be accurately estimated from the extracted features through a linear model. The successive steps of the process are as follow: first, decompose the spectrum using a wavelet packet (WP) and represent it by the derived decomposition coefficients; second, detect representative spectral features from the decomposition coefficients using the proposed method Least Absolute Shrinkage and Selection Operator (LARS){sub bs}; third, estimate the atmospheric parameters T{sub eff}, log g, and [Fe/H] from the detected features using a linear regression method. One prominent characteristic of this scheme is its ability to evaluate quantitatively the contribution of each detected feature to the atmospheric parameter estimate and also to trace back the physical significance of that feature. This work also shows that the usefulness of a component depends on both the wavelength and frequency. The proposed scheme has been evaluated on both real spectra from the Sloan Digital Sky Survey (SDSS)/SEGUE and synthetic spectra calculated from Kurucz's NEWODF models. On real spectra, we extracted 23 features to estimate T{sub eff}, 62 features for log g, and 68 features for [Fe/H]. Test consistencies between our estimates and those provided by the Spectroscopic Parameter Pipeline of SDSS show that the mean absolute errors (MAEs) are 0.0062 dex for log T{sub eff} (83 K for T{sub eff}), 0.2345 dex for log g, and 0.1564 dex for [Fe/H]. For the synthetic spectra, the MAE test accuracies are 0.0022 dex for log T{sub eff} (32 K for T{sub eff}), 0.0337 dex for log g, and 0.0268 dex for [Fe/H].

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Compact galaxies and the size–mass galaxy distribution from a colour-selected sample at 0.04 < z < 0.15 supplemented by ugrizYJHK photometric redshifts

ABSTRACT The size–mass galaxy distribution is a key diagnostic for galaxy evolution. Massive compact galaxies are potential surviving relics of a high-redshift phase of star formation. Some of these could be nearly unresolved in Sloan Digital Sky Survey (SDSS) imaging and thus not included in galaxy samples. To overcome this, a sample was selected from the combination of SDSS and UKIRT Infrared Deep Sky Survey (UKIDSS) photometry to r < 17.8. This was done using colour–colour selection, and then by obtaining accurate photometric redshifts (photo-z) using scaled flux matching (SFM). Compared to spectroscopic redshifts (spec-z), SFM obtained a 1σ scatter of 0.0125 with only 0.3 per cent outliers (|Δln (1 + z)| > 0.06). A sample of 163 186 galaxies was obtained with 0.04 < z < 0.15 over $2300\, {\rm deg}^2$ using a combination of spec-z and photo-z. Following Barro et al. log Σ1.5 = log M* − 1.5log r50, maj was used to define compactness. The spectroscopic completeness was 76 per cent for compact galaxies (log Σ1.5 > 10.5) compared to 92 per cent for normal-sized galaxies. This difference is primarily attributed to SDSS ‘fibre collisions’ and not the completeness of the main galaxy sample selection. Using environmental overdensities, this confirms that compact quiescent galaxies are significantly more likely to be found in high-density environments compared to normal-sized galaxies. By comparison with a high-redshift sample from 3D-HST, log Σ1.5 distribution functions show significant evolution, with this being a compelling way to compare with simulations such as EAGLE. The number density of compact quiescent galaxies drops by a factor of about 30 from z ∼ 2 to log (n/Mpc−3) = − 5.3 ± 0.4 in the SDSS–UKIDSS sample. The uncertainty is dominated by the steep cut off in log Σ1.5, which is demonstrated conclusively using this complete sample.

Baldry, Ivan K.↗
DOE OSTI2023

Constraining quenching time-scales in galaxy clusters by forward-modelling stellar ages and quiescent fractions in projected phase space

We forward-model mass-weighted stellar ages (MWAs) and quiescent fractions (f Q ) in projected phase space (PPS), using data from the Sloan Digital Sky Survey, to jointly constrain an infall quenching model for galaxies in log (M vir /M ⊙ ) > 14 galaxy clusters at z ∼ 0. We find the average deviation in MWA from the MWA–M⋆ relation depends on position in PPS, with a maximum difference between the inner cluster and infalling interloper galaxies of ∼1 Gyr. Our model employs infall information from N-body simulations and stochastic star-formation histories from the universemachine model. We find total quenching times of t Q = 3.7 ± 0.4 Gyr and t Q = 4.0 ± 0.2 Gyr after first pericentre, for 9 < log (M ⋆ /M ⊙ ) < 10 and 10 < log (M ⋆ /M ⊙ ) < 10.5 galaxies, respectively. By using MWAs, we break the degeneracy in time of quenching onset and time-scale of star formation rate (SFR) decline. We find that time of quenching onset relative to pericentre is $t_{\mathrm{delay}}=3.5^{+0.6}_{-0.9}$ Gyr and $t_{\mathrm{delay}}=-0.3^{+0.8}_{-1.0}$ Gyr for 9 < log (M ⋆ /M ⊙ ) < 10 and 10 < log (M ⋆ /M ⊙ ) < 10.5 galaxies, respectively, and exponential SFR suppression time-scales are τenv ≤ 1.0 Gyr for 9 < log (M ⋆ /M ⊙ ) < 10 galaxies and τenv ∼ 2.3 Gyr for 10 < log (M ⋆ /M ⊙ ) < 10.5 galaxies. Stochastic star formation histories remove the need for rapid infall quenching to maintain the bimodality in the SFR of cluster galaxies; the depth of the green valley prefers quenching onsets close to first pericentre and a longer quenching envelope, in slight tension with the MWA-driven results. Taken together these results suggest that quenching begins close to, or just after pericentre, but the time-scale for quenching to be fully complete is much longer and therefore ram-pressure stripping is not complete on first pericentric passage.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

WE-H-207A-03: The Universality of the Lognormal Behavior of [F-18]FLT PET SUV Measurements

Purpose: Log transforming [F-18]FDG PET standardized uptake values (SUVs) has been shown to lead to normal SUV distributions, which allows utilization of powerful parametric statistical models. This study identified the optimal transformation leading to normally distributed [F-18]FLT PET SUVs from solid tumors and offers an example of how normal distributions permits analysis of non-independent/correlated measurements. Methods: Forty patients with various metastatic diseases underwent up to six FLT PET/CT scans during treatment. Tumors were identified by nuclear medicine physician and manually segmented. Average uptake was extracted for each patient giving a global SUVmean (gSUVmean) for each scan. The Shapiro-Wilk test was used to test distribution normality. One parameter Box-Cox transformations were applied to each of the six gSUVmean distributions and the optimal transformation was found by selecting the parameter that maximized the Shapiro-Wilk test statistic. The relationship between gSUVmean and a serum biomarker (VEGF) collected at imaging timepoints was determined using a linear mixed effects model (LMEM), which accounted for correlated/non-independent measurements from the same individual. Results: Untransformed gSUVmean distributions were found to be significantly non-normal (p<0.05). The optimal transformation parameter had a value of 0.3 (95%CI: −0.4 to 1.6). Given the optimal parameter was close to zero (which corresponds to log transformation), the data were subsequently log transformed. All log transformed gSUVmean distributions were normally distributed (p>0.10 for all timepoints). Log transformed data were incorporated into the LMEM. VEGF serum levels significantly correlated with gSUVmean (p<0.001), revealing log-linear relationship between SUVs and underlying biology. Conclusion: Failure to account for correlated/non-independent measurements can lead to invalid conclusions and motivated transformation to normally distributed SUVs. The log transformation was found to be close to optimal and sufficient for obtaining normally distributed FLT PET SUVs. These transformations allow utilization of powerful LMEMs when analyzing quantitative imaging metrics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Characterization and identification of HPC applications at leadership computing facility

High Performance Computing (HPC) is an important method for scientific discovery via large-scale simulation, data analysis, or artificial intelligence. Leadership-class supercomputers are expensive, but essential to run large HPC applications. The Petascale era of supercomputers began in 2008, with the first machines achieving performance in excess of one petaflops, and with the advent of new supercomputers in 2021 (e.g., Aurora, Frontier), the Exascale era will soon begin. However, the high theoretical computing capability (i.e., peak FLOPS) of a machine is not the only meaningful target when designing a supercomputer, as the resources demand of applications varies. A deep understanding of the characterization of applications that run on a leadership supercomputer is one of the most important ways for planning its design, development and operation. In order to improve our understanding of HPC applications, user demands and resource usage characteristics, we perform correlative analysis of various logs for different subsystems of a leadership supercomputer. This analysis reveals surprising, sometimes counter-intuitive patterns, which, in some cases, conflicts with existing assumptions, and have important implications for future system designs as well as supercomputer operations. For example, our analysis shows that while the applications spend significant time on MPI, most applications spend very little time on file I/O. Combined analysis of hardware event logs and task failure logs show that the probability of a hardware FATAL event causing task failure is low. Combined analysis of control system logs and file I/O logs reveals that pure POSIX I/O is used more widely than higher level parallel I/O. Based on holistic insights of the application gained through combined and co-analysis of multiple logs from different perspectives and general intuition, we engineer features to "fingerprint" HPC applications. We use t-SNE (a machine learning technique for dimensionality reduction) to validate the explainability of our features and finally train machine learning models to identify HPC applications or group those with similar characteristic. To the best of our knowledge, this is the first work that combines logs on file I/O, computing, and inter-node communication for insightful analysis of HPC applications in production.

Liu, Zhengchun↗
DOE OSTI2016

The spectral energy distributions of Fermi blazars

In this paper, multiwavelength data are compiled for a sample of 1425 Fermi blazars to calculate their spectral energy distributions (SEDs). A parabolic function, log(νF{sub ν})=P{sub 1}(logν−P{sub 2}){sup 2}+P{sub 3}, is used for SED fitting. Synchrotron peak frequency (logν{sub p}), spectral curvature (P{sub 1}), peak flux (ν{sub p}F{sub ν{sub p}}), and integrated flux (νF{sub ν}) are successfully obtained for 1392 blazars (461 flat-spectrum radio quasars [FSRQs], 620 BL Lacs [BLs], and 311 blazars of uncertain type [BCUs]; 999 sources have known redshifts). Monochromatic luminosity at radio 1.4 GHz, optical R band, X-ray at 1 keV and γ-ray at 1 GeV, peak luminosity, integrated luminosity, and effective spectral indices of radio to optical (α{sub RO}) and optical to X-ray (α{sub OX}) are calculated. The “Bayesian classification” is employed to log ν{sub p} in the rest frame for 999 blazars with available redshift, and the results show that three components are enough to fit the log ν{sub p} distribution; there is no ultra-high peaked subclass. Based on the three components, the subclasses of blazars using the acronyms of Abdo et al. are classified, and some mutual correlations are also studied. Conclusions are finally drawn as follows: (1) SEDs are successfully obtained for 1392 blazars. The fitted peak frequencies are compared with common sources from available samples. (2) Blazars are classified as low synchrotron peak sources if log ν{sub p}(Hz)⩽14.0, intermediate synchrotron peak sources if 14.0< log ν{sub p}(Hz)⩽15.3, and high synchrotron peak sources if log ν{sub p}(Hz)>15.3. (3) Gamma-ray emissions are strongly correlated with radio emissions. Gamma-ray luminosity is also correlated with synchrotron peak luminosity and integrated luminosity. (4) There is an anticorrelation between peak frequency and peak luminosity within the whole blazar sample. However, there is a marginally positive correlation for high synchrotron peak BL Lacs (HBLs), and no correlations for FSRQs or low synchrotron peak BL Lacs (LBLs). (5) There are anticorrelations between the monochromatic luminosities (γ-ray and radio bands) and the peak frequency within the whole sample and BL Lacs. (6) The optical to X-ray (α{sub OX}) and radio to optical (α{sub RO}) spectral indices are strongly anticorrelated with peak frequency (log ν{sub p}) within the whole sample, but the correlations for subclasses of FSRQs, LBLs, and HBLs are different.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Fan Loops Observed by IRIS, EIS, and AIA

A comprehensive study of the physical parameters of active region fan loops is presented using the observations recorded with the Interface Region Imaging Spectrometer (IRIS), the EUV Imaging Spectrometer (EIS) on board Hinode, and the Atmospheric Imaging Assembly (AIA) and the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO). The fan loops emerging from non-flaring AR 11899 (near the disk center) on 2013 November 19 are clearly discernible in AIA 171 Å images and in those obtained in Fe viii and Si vii images using EIS. Our measurements of electron densities reveal that the footpoints of these loops are at an approximately constant pressure with electron densities of log N{sub e}=10.1 cm{sup −3} at log [T/K]=5.15 (O iv), and log N{sub e}=8.9 cm{sup −3} at log [T/K]=6.15 (Si x). The electron temperature diagnosed across the fan loops by means of EM-Loci suggest that two temperature components exist at log [T/K]=4.95 and 5.95 at the footpoints. These components are picked up by IRIS lines and EIS lines, respectively. At higher heights, the loops are nearly isothermal at log [T/K]=5.95, which remained constant along the loop. The measurement of the Doppler shift using IRIS lines suggests that the plasma at the footpoints of these loops is predominantly redshifted by 2–3 km s{sup −1} in C ii, 10–15 km s{sup −1} in Si iv, and 15–20 km s{sup −1} in O iv, reflecting the increase in the speed of downflows with increasing temperature from log [T/K]=4.40 to 5.15. These observations can be explained by low-frequency nanoflares or impulsive heating, and provide further important constraints on the modeling of the dynamics of fan loops.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2017

An Overview of Surface Heat Microbial Reduction as a Viable Microbial Reduction Modality for Spacecraft Surfaces

In accordance with NASA Planetary Protection (PP) policy requirements, flight project hardware may be required to undergo microbial reduction processes to prevent the forward contamination of target planetary bodies with Earth organisms. Heat microbial reduction (HMR) is the most commonly employed modality used at JPL for reducing the microbial bioburden on flight hardware. In 2013, longstanding HMR specifications were abandoned, and revised specifications were developed which integrated the latest findings on bacterial spore heat resistivity. Revised decimal reduction values (D-values) for time-temperature lethality curves (110 °C to 200 °C) were developed to account for “hardy” bacterial spores that exhibit greater heat resistance than previously understood. Presented here is a comparative analysis of the revised NASA HMR specifications against empirical data compiled from recent JPL studies, and peer-reviewed, published literature. Bacillus sp. strain ATCC 29669 displayed high heat resistance, and this strain’s 4-log heat lethality curve was comparable to the revised 4-log specification. Spores of Bacillus atrophaeus ATCC 9372 displayed less heat resistance, and exhibited D-values which were less than the revised 3-log microbial reduction specifications. Extrapolations indicate that the current 6-log reduction credit applied to 350 °C for 1 hr. and 500 °C for 0.5 sec. is highly conservative. Projections indicate that a 10- to 18-log reduction of both hardy and non-hardy spores may be achievable at bakeouts of 350 °C for 1 hr. The findings reported here indicate the revised NASA HMR specifications from 110 °C to 200 °C are appropriate for achieving 4-log and 6-log reductions with hardy spore populations; however, for non-hardy spore populations, or for temperatures above 200 °C, the specifications are exceedingly conservative.

Shirey, Brian T.↗
DOE OSTI2014

Potosi Reservoir Modeling; History and Recommendations

As a part of a larger project co-funded by the United States Department of Energy (US DOE) to evaluate the potential of formations within the Cambro-Ordovician strata above the Mt. Simon as potential targets for carbon sequestration in the Illinois and Michigan Basins, the Illinois Clean Coal Institute (ICCI) requested Schlumberger to evaluate the potential injectivity and carbon dioxide (CO₂) plume size of the Cambrian Potosi Formation. The evaluation of this formation was accomplished using wireline data, core data, pressure data, and seismic data from two projects: the US DOE-funded Illinois Basin–Decatur Project being conducted by the Midwest Geological Sequestration Consortium in Macon County, Illinois, as well as data from the Illinois – Industrial Carbon Capture and Sequestration (IL-ICCS) project funded through the American Recovery and Reinvestment Act. In 2010, technical performance evaluations on the Cambrian Potosi Formation were performed through reservoir modeling. The data included formation tops from mud logs, well logs from the Verification Well 1 (VW1) and the Injection Well (CCS1), structural and stratigraphic formation from three dimensional (3D) seismic data, and field data from several waste water injection wells for the Potosi Formation. The intention was for two million tonnes per annum (MTPA) of CO₂ to be injected for 20 years into the Potosi Formation. In 2013, updated reservoir models for the Cambrian Potosi Formation were evaluated. The data included formation tops from mud logs, well logs from the CCS1, VW1, and Verification Well 2 (VW2) wells, structural and stratigraphic formation from a larger 3D seismic survey, and field data from several waste water injection wells for Potosi Formation. The objective is to simulate the injection of CO₂ at a rate 3.5 million tons per annum (3.2 million tonnes per annum [MTPA]) for 30 years 106 million tons (96 MT total) into the Potosi Formation. The Potosi geomodeling efforts have evolved from using data from a single well in 2010 to the inclusion of data from three wells in 2013 which largely leverage the porosity and permeability logs plus knowledge of lost circulation zones. The first Potosi model (Potosi Geobody Model 2010) attempted to use the available seismic inversion data to inform the geomodel and predict vugular zones in advance of drilling VW1. Lost circulation zones in VW1 came in as the geologists predicted. The model was not implemented in subsequent simulation work. To date, reservoir models used for flow simulation work have relied predominately on Gaussian distributed properties (porosity and permeability) and have employed a single injection well. Potosi Model 2013b incorporated the new VW2 logs, and exhibited an extra level of sophistication by delineating the vugular intervals. This method added further realism that likely represents the best reservoir approximation to date. Where the 2010 reservoir models were 10 by 10 mi (16 by 16 km) in area, the 2013 models were expanded in size to 30 by 30 mi (48 by 48 km). The latest reservoir simulations show that a minimum of four injectors might be required to meet target injection rates. Still, there is data that requires further scrutiny and modeling methodologies that require testing for the Potosi Formation. This work is currently ongoing, and the next phase of the reservoir modeling intends to implement valuable data like porosity derived from seismic inversion, seismically derived geobodies, or a combination of both to further define vugular zones and the porosity distribution within the Potosi Formation. Understanding the dual porosity, dual permeability character of the Potosi remains the greatest challenge in representing this formation. Further analysis of the FMI* fullbore formation microimager data may aid in assessing this uncertainty. The Potosi Formation is indeed an interesting formation, and recommendations to further characterize it are included in the following list: - Data acquisition to identify the vugs permeability, distribution, and interconnectivity could be considered to perform a more rigorous evaluation of the Potosi Formation injectivity and capacity. This could be achieved by performing an injection test on a vugular interval to determine the vugs permeability, and an interference test between wells to evaluate the local vugs extent and interconnectivity. - A thorough study of the available FMI data may reveal specifics on estimating the vug to matrix ratio. This estimate could be used to further condition the porosity distribution. Porosity logs alone might underestimate the formation’s porosity associated with vugs. Porosity mapping derived from the seismic inversion could also be used in the succeeding task to characterize the lateral porosity distribution within the reservoir. This could involve the geobody methodology previously attempted in 2010. With or without seismic inversion porosity mapping, it is worth exploring whether increased lateral heterogeneity plays a significant role in Potosi injectivity. Investigations on vugular, dolomitic outcrops suggest that there may be significantly greater lateral heterogeneity than what has been modeled here. - The FMI data also reveals the presence of and helps describe open fractures. The presence of fractures will further enhance the formation’s permeability. The task of leveraging this data in the geomodeling effort still remains. Under the best of circumstances, this data describing open fractures may be combined with seismic attributes to delineate fracture corridors. Fracture modeling would certainly add another layer of sophistication to the model. Its contribution and applicability remain to be explored. - Facies modeling within the Potosi has yet to be thoroughly addressed. The carbonates during the time of deposition are believed to be regionally extensive. However, it may be worth delineating the reservoir with other regional wells or modern day analogues to understand the extent of the Potosi. More specifically, the model could incorporate lateral changes or trends if deemed necessary to represent facies transition. - Currently there is no fracture gradient data available for the Potosi in the Decatur project area. The acquisition of the fracture pressure data could be considered to determine an appropriate maximum allowable bottomhole injection pressure. This would allow the evaluation of injectivity and the required number of wells in a more precise manner. - Special core analysis (SCAL) to determine the relative permeability and capillary pressure of the vugs and matrix could be considered to have a better estimation of the reservoir injectivity and plume extent. - Formation water sampling and analysis could be considered for the Potosi to estimate the water salinity and properties. A vertical flow performance evaluation could be considered for the succeeding task to determine the appropriate tubing size, the required injection tubing head pressure, and to investigate whether the corresponding well injection rate falls within the tubing erosional velocity limit. - A simulation using several injectors could also be considered to determine the required number of wells to achieve the injection target while taking into account the pressure interference.

Smith, Valerie↗
DOE OSTI2015

Drilling, Completion, and Data Collection Plans An Assessment of Geological Carbon Sequestration Options in the Illinois Basin: Phase III

Executive Summary The Illinois Basin – Decatur Project (IBDP) is managed by the Midwest Geological Sequestration Consortium (MGSC) and is led by the Illinois State Geological Survey (ISGS) at the University of Illinois. The project site is located on the Archer Daniels Midland Company (ADM) property in Decatur, Illinois, and is a fully integrated carbon capture and storage (CCS) project that uses CO₂ captured from the ethanol-producing fermentation process at the ADM corn-processing plant (Finley et. al., 2013). IBDP has a goal of injecting one million tonnes of CO₂ into the basal sands of the Mt. Simon Sandstone over a three-year period. This is a multifaceted project, and this report details the planning and results of the drilling, completions, well testing, log data acquisition, and the Health, Safety, and Environment (HSE) aspects of the project. Three deep wells were planned for the IBDP: • The injection well: Injection Well #1 (CCS1); • The monitoring well (both in-zone and above seal): Verification Well #1 (VW1); and • The geophone monitoring well: Geophysical Monitoring Well #1 (GM1). The detailed plans for these wells are attached to the appendices of this document. The wells were drilled successfully with little deviation from the original plans. The biggest change from the plan to execution was the need to adjust for larger-than-expected loss of circulation in the Potosi section of the Knox Formation. The completions reports also attached to this document detail the well constructions as they were actually built. Injectivity testing was carried out, and the perforating plans were adjusted based on the results. Additional perforations and acidizing were performed as a result of the injectivity testing. The testing plans are detailed in this report along with the actual testing results. The injectivity testing results were used in the modeling and simulation efforts. Detailed HSE plans were developed and implemented during the planning and execution phases of the project. The implementation included an HSE Bridging Document, which served to unify the HSE policies of the project partners and key subcontractors. The HSE plan and actual HSE results are presented in this document. There were no recordable HSE incidents during the project. A detailed logging program was developed based on project needs. The log data were acquired in accordance with the plan, and both the plan and log results are presented in this report. Log data were heavily utilized by the research staff, modelers, reservoir engineers, and for technical and permitting efforts. 5 Several key lessons were learned during the project: • Safety in operations and execution is paramount and is only achieved through proper planning and behavior control. The certainty of this was reinforced through implementation of this lesson and the resultant flawless HSE performance during the project. • Losses of drilling fluid circulation were larger than anticipated within the Potosi Formation. Circulation was only recovered through cementing the loss zones. • When possible, minimizing complexity in permit requirements and well designs is preferable. • The size of the wells were outside of the standard experience and expertise typical within the basin, and therefore required substantial planning and ramp-up of contractors and partners to meet project objectives. • With multiple stakeholders and research partners, establishing objectives and requirements early and adhering to change request procedures throughout the project are critical to manage competing data and sampling objectives that may be detrimental to overall progress. The well construction and completion operations were successfully executed, with all wells built in a manner that achieved excellent wellbore integrity. Log planning involved a number of stakeholders and technical specialists. Data collection from logging, coring, and testing was excellent. Time and effort spent with the associated contractors and suppliers to develop a well plan beyond normal scope proved highly successful, resulting in a well-construction and completion project that surpassed expectations. The world-class HSE results also demonstrate the commitment of all stakeholders in the project. The details follow in the body of this document

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2015

Knowledge management information tool analytics with distributed database engine - 15737

The D and D KM-IT is a web-based knowledge management information tool custom built for the deactivation and decommissioning (D and D) user community. The system allows interested users to post information which goes through a custom work flow. To better improve the performance and user experience, the analysis of server logs is being conducted using MongoDB. MongoDB is a document database that provides high performance, high availability, and easy scalability. Objectives: - Feasibility study of Distributed Database Engine, MongoDB for web analytics; - Analyze server logs to evaluate D and D KM-IT module usage; - Generate useful information from the analysis. Method: - The server logs were loaded and stored in MongoDB for analysis; - Analysis and visualization was performed using NodeJS and Java Script library. Results: - It was possible to store log files in MongoDB; - Log files with different time stamps and groups from different servers can be aggregated; - Queries can be written to extract meaningful information from aggregated log files (e.g. users from different countries/organizations). Conclusions: - MongoDB is useful for server log analytics as the analyst can immediately access, analyze, and visualize MongoDB data based on the requirements; - The information can be extracted to develop custom reports; - Optimized queries can be run for fast data access. Path forward: - Custom queries need to be developed to extract information leading to improved user information.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Distributions of the Mean for Lognormally Distributed Data - 19427

In environmental sciences, a distribution of the mean is often needed for right-skewed data. For example, risk assessments are often based on distributions of the mean to obtain upper confidence limits (UCLs) for analyte concentrations, and, in probabilistic performance assessment (PPA) models, parameters are given distributions rather than deterministic values, and they are usually given distributions of their mean rather than the population distributions to account for the large spatial and temporal domains to which they are applied. How this distribution of the mean is developed depends on the characteristics of the parameter's full distribution and how the parameter relates to its use. In this presentation, I will discuss the implications of a right skew in distribution development. Parameters may vary over orders of magnitude and/or may be right-skewed. The material property of saturated hydraulic conductivity, Ks, is a prime example of such a parameter. Right-skewed parameters are often assumed to follow lognormal distributions. If X follows a lognormal distribution, then log(X) follows a normal distribution. For some right-skewed parameters, practitioners go back and forth between the two forms of the data depending on their needs. For example, it may be preferred to use the untransformed version of X in a model or equation, but it is easier to visualize the parameter values on a logarithmic scale. However, distribution development for the average value of the parameter will differ depending on whether X or log(X) is modeled. This difference is due to Jensen's Inequality, a theorem which states that the expected value of a concave function of a variable is less than or equal to the same function of the expected value of the variable; a logarithm is a concave function. In other words, the mean of log(X) is always less than or equal to the log of the mean of X and therefore the mean estimated from a distribution based on log(X) is less than or equal to the mean estimated from a distribution based on X. If a parameter is naturally considered on a log scale by subject matter experts, then distributions developed for the mean of log(X) may be more consistent with the use of the parameters in the literature than distributions developed for X. The statistical decisions of distribution development will be discussed within the context of right-skewed, lognormal data. These issues include method-of-moments versus maximum likelihood estimation of the distribution parameters and the use of bootstrapping to aid in the visualization of a distribution of the mean. The details surrounding Jensen's Inequality will be explained and visualized within the context of distribution development of a right-skewed parameter such as saturated hydraulic conductivity. (authors)

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Utah FORGE: Phase 2C Seismic Observation and Ground Water Well Data and Well Locations

This submission contains the following data associated with Utah FORGE Phase 2C within the Roosevelt Hot Springs geothermal area: - An ArcGIS shapefile with well locations for wells 58-32, 78-32, 68-32, OH-3, OH-4, WOW-2, and WOW-3. Well 58-32 is the deep test well, wells 68-32 and 78-32 are seismic test wells, and the OH and WOW wells were used for ground water monitoring and temperature gradient logging. - Drilling reports, lithologic logs, daily drilling reports, cuttings logs, and drilling photos for seismic monitoring wells 78-32 and 68-32. - Raw and post-processing temperature logs for wells OH-3, OH-4, WOW-2, and WOW-4. The archive also includes a README.txt file, which contains an explanation of the data and how it was acquired, and a text file with coordinates for the logged wells.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Application of machine learning to characterize gas hydrate reservoirs in Mackenzie Delta (Canada) and on the Alaska north slope (USA)

Here, artificial neural network-trained models were used to predict gas hydrate saturation distributions in permafrost-associated deposits in the Eileen Gas Hydrate Trend on the Alaska North Slope (ANS), USA and at the Mallik research site in the Beaufort-Mackenzie Basin, Northwest Territories, Canada. The database of Logging-While-Drilling (LWD) and wireline logs collected at five wells (Mount Elbert, Ignik Sikumi, and Kuparuk 7–11–12 wells at ANS, plus 2L-38 and 5L-38 wells at the Mallik research site) includes more than 10,000 depth points, which were used for training, validation, and testing the machine learning (ML) models. Data used in training the ML models include the well logs of density, porosity, electrical resistivity, gamma radiation, and acoustic wave velocity measurements. Combinations of two or three out of these five well logs were found to reliably predict the gas hydrate saturation with accuracy varying between 80 and 90% when compared to the gas hydrate saturations derived from Nuclear Magnetic Resonance (NMR)-based technique. The ML models trained on data from three ANS wells achieved high fidelity predictions of gas hydrate saturation at the Mallik site. The results obtained in this study indicate that ML models trained on data from one geological basin can successfully predict key reservoir parameters for permafrost-associated gas hydrate accumulations within another basin. A generalized approach for selecting a well log combination that can improve model accuracy is discussed. Overall, the study outcome supports earlier work demonstrating that ML models trained on non-NMR well logs are a viable alternative to physics-driven methods for predicting gas hydrate saturations.

58 GEOSCIENCES↗
DOE OSTI2019

Use of the ERD for administrative monitoring of Theta

Monitoring the state of an HPC cluster in a timely and accurate fashion is critical to most system administration functions. For many Cray users, the first step in monitoring is ingestion of log files. Unfortunately, log parsing is an inherently inefficient process, requiring multiple software components to read and write from files on disk. Cray's own utilities use a message bus, the Event Router Daemon (ERD), for a wide variety of purposes. At the Argonne Leadership Computing Facility (ALCF), we have begun to use this message bus for monitoring via a client library written in Go, allowing us to read in structured data directly from Cray's services and, in many instances, bypass log files entirely. In this paper, we will examine the implementation and utilization of this approach on our 4392 node XC40, Theta, as well as the overall benefits and drawbacks to using the ERD for real-time monitoring.

Cray↗