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All sources NASA NTRS DOE OSTI

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 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↗

DOE OSTI2019

Complexation of lanthanides and other metal ions by the polypyridyl ligand quaterpyridine: Relation between metal ion size, chelate ring size, and complex stability

Metal ion complexing properties of the ligand QPY (2,2':6',2'':6'',2'''-quaterpyridine) are reported in relation to its possible use as a functional group for reagents in the extraction and separation of Ln(III) (lanthanide) ions. QPY is almost totally insoluble in water, but variation of the intense π-π* transitions in the UV spectrum of 1 × 10 –5 M QPY solutions in 50% CH 3 OH/H 2 O (v/v) as a function of pH or metal ion concentration allowed for the determination of the protonation constant (pK) and log K 1 values with metal ions. The pK a values of QPY are 4.54 ± 0.02 and 3.47 ± 0.02 in 50% CH 3 OH/H 2 O. The log K 1 values determined by UV–visible spectroscopy for the Ln(III) ions by titration of 1:1 solutions of 1.0 × 10 5 M Ln(III) ion and QPY between pH 2 and 6 were: La(III), 3.79; Pr(III), 4.34; Nd(III) 4.57; Sm(III), 4.78; Gd(III) 4.69; Dy(III), 4.76, Ho(III), 4.94; Er(III), 5.36, Tm(III), 5.84, and Lu(III) 5.54, all at 25 °C and ionic strength zero. The log K 1 values for the Ln(III) ions show quite strong increases from La(III) to Lu(III), with a local maximum at Sm(III), and a local minimum at Gd(III). The local maxium at Sm(III) is attributed to this ion being the best-fit size for coordinating with polypyridyl ligands. The effect of preorganization of polypyridyls such as QPY with bridging benzo groups in the ligand backbone to yield DPP (2,9-dipyrid-2-yl-1,10-phenanthroline) is to produce significant increases in log K 1 for large metal ions such as Ln(III) ions, but for small metal ions such as Zn(II), there is a large decrease in log K 1 in passing from QPY to DPP: this shows how rigidification of the ligand can enhance the selectivity against small metal ions of polypyridyls forming five-membered chelate rings. DFT calculations are used to analyze the variation in log K 1 in passing from the La(III) complexes of QPY to DPP compared to those for the smaller Lu(III) ion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Sparse Cholesky factorization for solving nonlinear PDEs via Gaussian processes

In recent years, there has been widespread adoption of machine learning-based approaches to automate the solving of partial differential equations (PDEs). Among these approaches, Gaussian processes (GPs) and kernel methods have garnered considerable interest due to their flexibility, robust theoretical guarantees, and close ties to traditional methods. They can transform the solving of general nonlinear PDEs into solving quadratic optimization problems with nonlinear, PDE-induced constraints. However, the complexity bottleneck lies in computing with dense kernel matrices obtained from pointwise evaluations of the covariance kernel, and its partial derivatives, a result of the PDE constraint and for which fast algorithms are scarce. The primary goal of this paper is to provide a near-linear complexity algorithm for working with such kernel matrices. We present a sparse Cholesky factorization algorithm for these matrices based on the near-sparsity of the Cholesky factor under a novel ordering of pointwise and derivative measurements. The near-sparsity is rigorously justified by directly connecting the factor to GP regression and exponential decay of basis functions in numerical homogenization. We then employ the Vecchia approximation of GPs, which is optimal in the Kullback-Leibler divergence, to compute the approximate factor. This enables us to compute ϵ-approximate inverse Cholesky factors of the kernel matrices with complexity O(N log d (N/ϵ)) in space and O(N log 2d (N/ϵ)) in time. We integrate sparse Cholesky factorizations into optimization algorithms to obtain fast solvers of the nonlinear PDE. We numerically illustrate our algorithm’s near-linear space/time complexity for a broad class of nonlinear PDEs such as the nonlinear elliptic, Burgers, and Monge-Ampère equations. In summary, we provide a fast, scalable, and accurate method for solving general PDEs with GPs and kernel methods.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

SHαDE: survey description and mass–kinematics scaling relations for dwarf galaxies

ABSTRACT The Study of H α from Dwarf Emissions (SHαDE) is a high spectral resolution (R = 13 500) H α integral field survey of 69 dwarf galaxies with stellar masses 106 < M⋆ < 109 M⊙. The survey used FLAMES on the ESO Very Large Telescope. SHαDE is designed to study the kinematics and stellar populations of dwarf galaxies using consistent methods applied to massive galaxies and at matching level of detail, connecting these mass ranges in an unbiased way. In this paper, we set out the science goals of SHαDE, describe the sample properties, outline the data reduction, and analysis processes. We investigate the log M⋆–log S0.5 mass–kinematics scaling relation, which has previously shown potential for combining galaxies of all morphologies in a single scaling relation. We extend the scaling relation from massive galaxies to dwarf galaxies, demonstrating this relation is linear down to a stellar mass of M⋆ ∼ 108.6 M⊙. Below this limit, the kinematics of galaxies inside one effective radius appears to be dominated by the internal velocity dispersion limit of the H α-emitting gas, giving a bend in the log M⋆–log S0.5 relation. Replacing stellar mass with total baryonic mass using gas mass estimate reduces the severity but does not remove the linearity limit of the scaling relation. An extrapolation to estimate the galaxies’ dark matter halo masses, yields a log Mh–log S0.5 scaling relation that is free of any bend, has reduced curvature over the whole mass range, and brings galaxies of all masses and morphologies on to the virial relation.

Barat, Dilyar↗

DOE OSTI2023

Abundances of CNO elements in z ∼ 0.3–0.4 Lyman continuum leaking galaxies

We present observations with the Space Telescope Imaging Spectrograph onboard the Hubble Space Telescope (HST) of 11 Lyman continuum (LyC) leaking galaxies at redshifts, z , in the range 0.29–0.43, with oxygen abundances 12+log(O/H) = 7.64–8.16, stellar masses M ⋆ ∼ 10 7.8 –10 9.8 M ⊙ , and O 32 = [O III ] λ5007/[O II ] λ3727 of ∼ 5–20 aiming to detect the C III ] λ1908 emission line. We combine these observations with the optical Sloan Digital Sky Survey (SDSS) spectra for the determination of carbon, nitrogen, and oxygen abundances. Our sample was supplemented by 31 galaxies from the literature, for which carbon, nitrogen, and oxygen abundances can be derived from the HST and SDSS spectra. These additional galaxies, however, do not have LyC observations. We find that log(C/O) for the entire sample at 12+log(O/H) < 8.1 does not depend on metallicity, with a small dispersion of ∼0.13 dex around the average value of ∼−0.75 dex. On the other hand, the log(N/O) in galaxies at z > 0.1, including LyC leakers, is systematically higher compared to the rest of the sample with lower metallicity. We find that log(C/O) slightly decreases with increasing M ⋆ from ∼ −0.65 at M ⋆ = 10 6 M ⊙ to ∼ −0.80 at M ⋆ = 10 9 –10 10 M ⊙ , whereas log(N/O) is considerably enhanced at M ⋆ > 10 8 M ⊙ . The origin of these trends remains basically unknown. A possible solution would be to assume that the upper mass limit of the stellar initial mass function in more massive galaxies is higher. This would result in a higher production of oxygen and a larger fraction of massive stars with stellar wind polluting the interstellar medium with nitrogen.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

A comparison of the baryonic Tully–Fisher relation in MaNGA and IllustrisTNG

We compare an observed baryonic Tully–Fisher relation (BTFR) from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) and HI-MaNGA surveys to a simulated BTFR from the cosmological magnetohydrodynamical simulation, IllustrisTNG. To do so, we calibrate the BTFR of the local universe using 377 galaxies from the MaNGA and HI-MaNGA surveys, and perform mock 21 cm observations of matching galaxies from IllustrisTNG. The mock observations are used to ensure that the comparison with the observed galaxies is fair since it has identical measurement algorithms, observational limitations, biases, and uncertainties. For comparison, we also calculate the BTFR for the simulation without mock observations and demonstrate how mock observations are necessary to fairly and consistently compare between observational and theoretical data. We report a MaNGA BTFR of log$_{10} (M_{ \rm Bary}/M_\odot)= (2.97 \pm 0.18)$ log$_{10} V_{ \rm Rot} + (4.04 \pm 0.41)\, \log _{10}{M_{\odot }}$ and an IllustrisTNG BTFR of log$_{10} (M_{ \rm Bary}/M_\odot) = (2.94 \pm 0.23$) log$_{10} V_{ \rm Rot} + (4.15 \pm 0.44)\, \log _{10}{M_{\odot }}$. Thus, MaNGA and IllustrisTNG produce BTFRs that agree within uncertainties, demonstrating that IllustrisTNG has created a galaxy population that obeys the observed relationship between mass and rotation velocity in the observed universe.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Sentiment Analysis based Error Detection for Large-Scale Systems

Today's large-scale systems such as High Performance Computing (HPC) Systems are designed/utilized towards exascale computing, inevitably decreasing its reliability due to the increasing design complexity. HPC systems conduct extensive logging of their execution behaviour. In this paper, we leverage the inherent meaning behind the log messages and propose a novel sentiment analysis-based approach for the error detection in large-scale systems, by automatically mining the sentiments in the log messages. Our contributions are four-fold. (1) We develop a machine learning (ML) based approach to automatically build a sentiment lexicon, based on the system log message templates. (2) Using the sentiment lexicon, we develop an algorithm to detect system errors. (3) We develop an algorithm to identify the nodes and components with erroneous behaviors, based on sentiment polarity scores. (4) We evaluate our solution vs. other state-of-the-art machine/deep learning algorithms based on three representative supercomputers' system logs. Experiments show that our error detection algorithm can identify error messages with an average MCC score and f -score of 91% and 96% respectively, while state of the art ML/deep learning model (LSTM) obtains only 67% and 84%. To the best of our knowledge, this is the first work leveraging the sentiments embedded in log entries of large-scale systems for system health analysis.

error detection↗

DOE OSTI2018

Spatio-Temporal Analysis of HPC I/O and Connection Data

The HPC system consists of a set of layers of software and hardware for I/O and networking. System logs are helpful resources to understand what is going on in the system. A challenge is that it is non-trivial to analyze the logs maintained in various levels of the stack. Independent analysis might lead to an incomplete conclusion due to the limited coverage of each log. This work takes a comprehensive approach to analysis that incorporates the logs in the multiple layers and components, in order to facilitate the detection of anomalous activities. This research aims to identify and predict potential performance bottlenecks in the HPC system, by capturing the temporal variation patterns from heterogeneous, high-dimensional, and non-linear log data. In this paper, we share our preliminary efforts for spatio-temporal analysis of HPC I/O and connection data, with our initial observations from the analysis of one-week HPC log data sets collected from one of NERSC systems.

Choi, Jinhwan↗

DOE OSTI2016

SU-G-JeP1-08: Dual Modality Verification for Respiratory Gating Using New Real- Time Tumor Tracking Radiotherapy System

Purpose: The respirato ry gating system combined the TrueBeam and a new real-time tumor-tracking radiotherapy system (RTRT) was installed. The RTRT system consists of two x-ray tubes and color image intensifiers. Using fluoroscopic images, the fiducial marker which was implanted near the tumor was tracked and was used as the internal surrogate for respiratory gating. The purposes of this study was to develop the verification technique of the respiratory gating with the new RTRT using cine electronic portal image device images (EPIDs) of TrueBeam and log files of the RTRT. Methods: A patient who underwent respiratory gated SBRT of the lung using the RTRT were enrolled in this study. For a patient, the log files of three-dimensional coordinate of fiducial marker used as an internal surrogate were acquired using the RTRT. Simultaneously, the cine EPIDs were acquired during respiratory gated radiotherapy. The data acquisition was performed for one field at five sessions during the course of SBRT. The residual motion errors were calculated using the log files (E{sub log}). The fiducial marker used as an internal surrogate into the cine EPIDs was automatically extracted by in-house software based on the template-matching algorithm. The differences between the the marker positions of cine EPIDs and digitally reconstructed radiograph were calculated (E{sub EPID}). Results: Marker detection on EPID using in-house software was influenced by low image contrast. For one field during the course of SBRT, the respiratory gating using the RTRT showed the mean ± S.D. of 95{sup th} percentile E{sub EPID} were 1.3 ± 0.3 mm,1.1 ± 0.5 mm,and those of E{sub log} were 1.5 ± 0.2 mm, 1.1 ± 0.2 mm in LR and SI directions, respectively. Conclusion: We have developed the verification method of respiratory gating combined TrueBeam and new real-time tumor-tracking radiotherapy system using EPIDs and log files.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

Application of a new hybrid particle swarm optimization-mixed kernels function-based support vector machine model for reservoir porosity prediction: A case study in Jacksonburg-Stringtown oil field, West Virginia, USA

Porosity is a fundamental property that characterizes the storage capability of fluid and gas-bearing formations in a reservoir. An accurate porosity value can be measured from core samples in the laboratory; however, core analysis is expensive and time consuming. Well-log data can be used to calculate porosity, but the availability of log suites is often limited in mature fields. Therefore, robust porosity prediction requires integration of core-measured porosity with available well-log suites to control for changes in lithology and fluid content. A support vector machine (SVM) model with mixed kernel function (MKF) is used to construct the relationship between limited conventional well-log suites and sparse core data. Porosity is the desired output, and two conventional well-log responses (gamma ray [GR] and bulk density) and three well-log-derived parameters (the slope of GR, the slope of density, and [Formula: see text]) are input parameters. A global stochastic searching algorithm, particle swarm optimization (PSO), is applied to improve the efficiency of locating the appropriate values of five control parameters in MKF-SVM model. The results of SVM with different traditional kernel functions were compared, and the MKF-SVM model provided an improvement over the traditional SVM model. To confirm the advantage of the hybrid PSO-MKF-SVM model, the results from three models: (1) radial basis function (RBF)-based least-squares SVM, (2) multilayer perceptron artificial neural network (ANN), and (3) RBF ANN, are compared with the result of the hybrid PSO-MKF-SVM model. The results indicate that the hybrid PSO-MKF-SVM model improves porosity prediction with the highest correlation coefficient ([Formula: see text] of 0.9560), the highest coefficient of determination ([Formula: see text] of 0.9140), the lowest root-mean-square error (1.6505), average absolute error value (1.4050), and maximum absolute error (2.717).

Geochemistry & Geophysics↗

DOE OSTI2020

Multi-Scale Lithofacies and Chemostratigraphic Analysis of Two Middle Devonian Marcellus Shale Wells in Northern West Virginia, USA

The Marcellus Shale Energy and Environmental Laboratory (MSEEL) consists of two project areas within the dry gas producing region of the Marcellus shale play in Monongalia County, West Virginia. MSEEL is a collaborative field project led by West Virginia University, with Northeast Natural Energy LLC, several industrial partners, and sponsored by the US Department of Energy National Energy Technology Laboratory. The study areas are drilled approximately 8.5 miles apart to better understand the vertical and lateral changes in stratigraphy over a short distance. Two vertical pilot wells, MIP-3H and Boggess 17H were drilled in the fall of 2015 and spring of 2019, respectively. Core was recovered from the MIP-3H (API: 47-061-01707-00-00) 112 feet (34m) between depths of 7445 to 7557 feet, and from the Boggess 17H (API: 47-061-01812-00-00) 139 feet (42m) between depths of 7908 and 8012 ft. A full suite of triple combo (gamma ray, neutron, density logs), image logs, and advanced logging tools were run in both wells and calibrated to core analysis. Core analysis includes medical computed tomography (CT) scans, mineralogy and chemostratigraphy determined from handheld X-Ray fluorescence (hhXRF) and X-Ray powder diffraction (XRD) measurements, and determination of total organic content (TOC). Lithofacies were determined at core-scale using traditional core description techniques and medical CT-scan images. Log-scale facies are based on mineralogy and TOC data and developed using petrophysical logging data calibrated to core data (XRD and pyrolysis data). Chemostratigraphic analysis utilized hhXRF data to determine the major and trace element trends in the cores. In the two wells six shale lithofacies were recognized at the core and log scale. Both wells show organic-rich facies (TOC > 6.5%) primarily in the middle and lower Marcellus, with a slight decrease in thickness of this interval in the Boggess 17H. This interval is interpreted as an increase in paleo-productivity (increased Ni, Zn, and V), decreased sedimentation (decreased detrital proxies), and anoxic to euxinic conditions (increased Mo and chalcophile elements). Paleo-redox conditions in both wells are dynamic throughout deposition transitioning between euxinic/anoxic to dysoxic/oxic. This trend is seen through elemental proxies and calcite/pyrite concretion distributions.

03 NATURAL GAS↗

DOE OSTI2019

Frio 2 : Raw CASSM Datasets

This submission contains the raw SEG-2 seismic datasets acquired in the CASSM experiment, part of the Frio-2 GCS pilot. The directory also includes the log files generated by the Geode seismograph (geometrics) used during acquisition. All SEG-2 files have a ".dat" suffix and log files have a ".log" suffix. Various supplementary files are also included (see below) including a set of flow and seismic models used in interpretation. The earlyGathers.tar.gz resource includes raw data acquired during system testing and pre-injection periods. All files correspond to the single source continuous active source seismic monitoring (CASSM) dataset described in the following papers. Daley, T.M., Solbau, R.D., Ajo-Franklin, J.B., and Benson, S.M. 2007, “Continuous Active-Source Seismic Monitoring of CO2 Injection in a Brine Aquifer”, Geophysics, Vol. 72, No. 5, p. A57-A61 Daley, T., Ajo-Franklin, J.B., and C. Doughty, 2011, “Constraining the reservoir model of an injected CO2 plume with crosswell CASSM at the Frio-II Brine Pilot”, International Journal of Greenhouse Gas Control (IJGGC), Vol. 5, No. 2, pp. 1022-1030. Zhu, T., Ajo-Franklin, J., and T. Daley, 2017, “Spatio-temporal changes in seismic attenuation caused by injected CO2 at the Frio-II pilot site, Dayton TX, USA”, Journal of Geophysical Research – Solid Earth, Vol. 122, No. 9, pp. 7156-7171. cassmDataRaw.tar : The main tar file containing all of the SEG-2 field records and the Geode log files. Stacking, time sampling (dt), and date information should be in the .log file. Each file corresponds to a single stacked gather for the single source deployed. Data was recorded on a 24 channel downhole hydrophone array. fieldNotes.tar : contains some of the scanned observer logs from the experiments as well as some planning images fieldPhotos.tar : contains a small set of photographs taken during the experiment frio_flow_seismic_models.mat : a matlab structure containing a set of flow and seismic models used in interpreting the CASSM data (see IJGGC paper).

GCS↗