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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Advancing subsurface analysis: Integrating computer vision and deep learning for the near real-time interpretation of borehole image logs in the Illinois Basin-Decatur Project

The accurate quantification and mapping of subsurface natural fracture systems using borehole imaging logs are critical for the success of CO 2 sequestration in geologic formations, optimization of engineered geothermal systems, and hydrocarbon production enhancement. However, traditional interpretation processes suffer from time-consuming procedures and human bias. To address these challenges and expedite fracture analysis, we investigated the application of integrated computer vision and DL workflows to automate image log analysis. Specifically, the design of our workflow was crafted to swiftly detect fractures and baffles by using actual electrical resistivity of borehole wall from microresistivity imaging device alongside their binary representation. This novel approach significantly reduces computational time while providing invaluable insights. By incorporating conventional logging and microseismic data, we present a regional subsurface natural fracture mapping technique. Through the minimization of human bias in image log analysis, our automated workflow achieves reduced fracture interpretation time and costs while ensuring robust and reproducible results. We demonstrated the efficacy of our approach by applying the workflow to the Illinois Basin-Decatur Project site. The automated workflow successfully identified major fractured zones, multiple baffles, and an interbedded layer with a high resolution of 0.01 ft or 0.12 in. (0.3 cm) and can be upscaled to any desired resolution. Validation through microseismic and image log interpretations allows for accurate and near-real-time mapping of fractures and baffles, significantly enhancing CO 2 pressure forecasting and postinjection site care. Our approach stands out due to its robustness, consistency, and reduced computational cost compared with alternative feature extraction technologies. It presents exciting possibilities for advancing CO 2 sequestration and engineered geothermal efforts by offering comprehensive and efficient fracture mapping solutions. This technology can contribute significantly to the optimization of CO 2 sequestration projects, facilitating sustainable environmental practices, and combating climate change.

Geochemistry & Geophysics↗
DOE OSTI2021

Virtual Log-Structured Storage for High-Performance Streaming

Over the past decade, given the higher number of data sources (e.g., Cloud applications, Internet of things) and critical business demands, Big Data transitioned from batch-oriented to real-time analytics. Stream storage systems, such as Apache Kafka, are well known for their increasing role in real-time Big Data analytics. For scalable stream data ingestion and processing, they logically split a data stream topic into multiple partitions. Stream storage systems keep multiple data stream copies to protect against data loss while implementing a stream partition as a replicated log. This architectural choice enables simplified development while trading cluster size with performance and the number of streams optimally managed. This paper introduces a shared virtual log-structured storage approach for improving the cluster throughput when multiple producers and consumers write and consume in parallel data streams. Stream partitions are associated with shared replicated virtual logs transparently to the user, effectively separating the implementation of stream partitioning (and data ordering) from data replication (and durability). We implement the virtual log technique in the KerA stream storage system. When comparing with Apache Kafka, KerA improves the cluster ingestion throughput by up to 4x when multiple producers write over hundreds of data streams.

consistent stream ordering↗
DOE OSTI2018

SECARB Cranfield Well Logs

Borehole logs from the SECARB project at the Cranfield oil site in Franklin, Mississippi . Well logs are a part of the geologic characterization phase of SECARB; CFU 31F-1, CFU 31F-2, CFU 31F-3 well logs from Detailed Area of Study (DAS) and other well logs. Associated Publications: Butsch, R., Brown, A. L., Bryans, B., Kolb, C., Hovorka, S. D., 2013, Integration of well-based subsurface monitoring technologies: lessons learned at SECARB study, Cranfield, MS: International Journal of Greenhouse Gas Control https://doi.org/10.1016/j.ijggc.2013.06.010.

Borehole log↗
DOE OSTI2023

Logging Crew Attributes by Region in the Southeast USA

Urbanization, shrinking markets, and reduced forestry investment may affect harvesting efficiency in regions of the US South. To monitor these conditions, logging businesses have been tracked by surveys conducted by universities and trade associations. This project used a sampling approach coordinated with FIA utilization studies to sample logging crews based on a harvesting location. The approach was used to develop relationships among firm attributes and site attributes in six southeastern states (AL, GA, FL, NC, SC, and VA) from 2011 to 2018. The data included harvest attributes (location, harvest size and stand type) and logging firm attributes (production, crew labor, crew number, the number of machines by type, and machine age). For crew capital value, an equation was developed for this study using machine number and average machine age. The data from logging crews on 419 harvests were analyzed by region, harvest size, and stand type. Mean values for crew labor ranged from 3.1 to 7.1 workers. The average capital value per crew ranged from $\$220,000$ to $\$524,000$ per crew in the Coastal Plain with a narrower range in the Piedmont. In the Coastal Plain, higher productivity was detected for larger harvests and pine versus hardwood and mixed stands; however, in the Piedmont those trends were less obvious. Ratio of feller-bunchers, skidders and loaders were mostly 1:1:1 or 1:2:1 with 41% and 24% of samples, respectively. There were notable trends among Coastal Plain loggers regarding capital value and productivity with evidence supported by a production function. The differences in Piedmont (e.g., ownership size, market access, terrain, population density, etc.) may combine to limit daily production and labor productivity.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1963

A THEORETICAL AND EXPERIMENTAL INVESTIGATION OF FLUSH MOUNTING CONFIGURATIONS FOR LOG PERIODIC STRUCTURES

Many types of log periodic structures have been described in the literature; but, as far as is known, flush-mounted log periodic antennas have not been studied heretofore. The purpose of this study is to investigate, primarily by experimental methods, the input impedance and radiation characteristics of a particular type of log periodic antenna when flush-mounted over a suitable cavity or recess in a conducting surface. The particular type of log periodic antenna chosen for use in the investigation is referred to as a monopole-tooth antenna and is described in detail in Part II of the report. A suitable cavity over which the monopole-tooth antenna may be mounted is described in Part III, and experimentally determined characteristics of the antenna-cavity assembly are presented in Part IV. The first objective of the study was to obtain an optimum or nearoptimum design of the antenna-cavity assembly. This objective has been reached within the limits of the experimental approach, and the results obtained are summarized in Part V of the report. A second objective, which will be reported upon at a future date, is to establish optimum design criteria and performance characteristics of the antenna-cavity assembly when the cavity is filled with low-loss dielectric material.

RADIATION FUNCTION↗
DOE OSTI2021

Combined Structural Analysis of Core and Image Log of TGH 76-31 South East of Mt Baker, Washington State

Despite active volcanism, few geothermal energy resources have been developed in the Cascades Range. Temperature Gradient Hole 76-31 was drilled to ~440 m measured depth to probe for zones where fractures provide fluid conduits that transport deep volcanic heat to shallow depths that could support baseload, carbon neutral electrical generation. These zones were predicted by a Play Fairway assessment (PFA) of resource potential along a zone 11 km west-southwest of the summit of Mount Baker Volcano. Rock core, temperature logs, and an acoustic image log were obtained. By comparison to outcrops, the core has been initially interpreted as the Chilliwack group comprised of partially metamorphosed basaltic to andesitic volcanics. Core mapping reveals complex, steeply dipping networks of fractures and brecciation along slickensided strike slip faults; clay alteration is common in many of these structures. The majority of fractures are thoroughly healed by layers of chlorite and calcite; preservation of pyrite indicates anoxic conditions. The majority of fracture porosity resides in very dense fractures a few centimeters or less in length. The image log provides good insights into attitude of fractures that fully transect the core, but generally underestimates fracture density. The combination of complex, non-planar fracture zones containing many short fractures and healing promote misinterpretation of natural fracture attitude and density in the image log. The healing and anoxic minerals are consistent with the conductive temperature gradient measured in the well below a shallow isothermal zone, although, several fractures are open or only partially healed and resulted in fluid entries into the well. Here, the equilibrated measured temperature gradient of 64ºC/km and calculated heat flow of 145 mW/m2 is more than twice the regional average, indicating local influence of the Mt Baker magmatic system at the Little Park Creek TGH site.

Cascades↗
DOE OSTI2018

SLoG: Large-Scale Logging Middleware for HPC and Big Data Convergence

Cloud developers traditionally rely on purpose-specific services to provide the storage model they need for an application. In contrast, HPC developers have a much more limited choice, typically restricted to a centralized parallel file system for persistent storage. Unfortunately, these systems often offer low performance when subject to highly concurrent, conflicting I/O patterns. This makes difficult the implementation of inherently concurrent data structures such as distributed shared logs. Yet, this data structure is key to applications such as computational steering, data collection from physical sensor grids, or discrete event generators. In this paper we tackle this issue. We present SLoG, shared log middleware providing a shared log abstraction over a parallel file system, designed to circumvent the aforementioned limitations. We evaluate SLoG’s design on up to 100,000 cores of the Theta supercomputer: the results show high append velocity at scale while also providing substantial benefits for other persistent backend storage systems.

big data↗
DOE OSTI2018

Fallon FORGE: Well 21-31 Wireline Logs

Included are the following wireline logs from the Fallon FORGE Well 21-31: Ormat_Forge 21-31_8.5 inch section: FMI data in PDF and DLIS formats; MSCT data in PDF format; Pressure and Temperature data in PDF, LAS, and DLIS formats; Sonic Scanner data in PDF, DLIS, and LAS formats (LAS format contains Stoneley Slowness, Shear Slowness, and Compressional Slowness logs); Triple Combo in PDF, DLIS, and LAS formats; and USIT data in DLIS and PDF formats. Ormat_Forge_21-31_12.25 inch section: Resistivity data in PDF and LAS formats; Sonic Scanner data in PDF, DLIS, and LAS formats (LAS format contains Stoneley Slowness, Shear Slowness, and Compressional Slowness logs); Triple Combo in PDF and LAS formats; and Caliper data in PDF format (DLIS format for caliper data is included in the Sonic Scanner DLIS).

15 GEOTHERMAL ENERGY↗
DOE OSTI2018

Fallon FORGE: Well Log Database

Master well database for all logs and other well data pertaining to the Fallon FORGE site. Tables are formatted in NGDS Teir 3 schema where appropriate; redundancies between tables are omitted, and should be queried off of the main WellHeader table. All relationships are set on the "HeaderURI" primary key. Tables: WellHeader - well locations and information ChemFluid - aqueous chemistry analyses ChemRock - rock chemistry analyses DensityCore - rock densities from core FracturesInduced - induced borehole fractures FracturesNatural - natural borehole factures LithIntercepts - lithology picks from cuttings and core MagSuscCore - magnetic susceptibility for core samples MagSuscPmag - magnetic susceptibility for pmag samples PTSLogs - pressure-temperature-spin logs WirelineLogs - digital wireline logs (21-31 only)

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Informing Plant Asset Reliability and Availability Through AI-Driven Analysis of Operator Logs

The availability and reliability of nuclear power plant (NPP) structures, systems, and components (SSCs) are critical parameters for NPP safety. Tracking these parameters is necessary but costly and labor-intensive, requiring the collection and evaluation of SSC event data such as shutdowns, startups, and failures. To show how these events are needed for the parameters an example is given: one measure of reliability is based on the number of equipment failure events and the number of run hours (i.e., the time from a startup event to a shutdown event). Here, this work investigates using artificial intelligence (AI) to mine NPP operator log entry texts for SSC event data. Four AI approaches were explored for identifying these events, including natural language processing (NLP) methods, generative AI, generative AI combined with NLP, and topic modeling. A key challenge addressed with all four approaches is the brevity of operator log entries. Among these four a neural network–based NLP method was shown to be the most promising for this application, achieving F1 scores of 86.0% for shutdowns, 92.2% for startups, and 80.4% for failures on a subject-matter-expert-curated dataset from NPP operator logs, compared to a baseline of 66.6% for a random classifier. This shows that NLP methods can perform better than generative AI. Additionally, the NLP methods combined with generative AI were shown to perform better than generative AI alone. Generative AI was most successful at providing the background information for the NLP methods to use. This work demonstrates the potential to use AI to automate parameter collection from NPP operator log entries and other records.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2019

FTLADS: Object-Logging Based Fault-Tolerant Big Data Transfer System Using Layout Aware Data Scheduling

The layout-aware data scheduling (LADS) data movement framework optimizes congestion for end-to-end data transfers. During data transfer, LADS can avoid congested storage elements by exploiting the underlying storage layout at each endpoint. This improves the I/O bandwidth and hence the data transfer rate across high-speed networks. However, the absence of fault tolerance (FT) in LADS results in data retransmission overhead and may lead to possible data integrity issues upon faults. In this paper, we propose object-logging FT mechanisms to avoid transmitting the objects that are successfully written into the parallel file system (PFS) at the sink end. Depending on the number of log files created for the whole dataset, we have classified our FT mechanisms into three different categories: file logger, transaction logger, and universal logger. Also, to address the space overhead, we have proposed different methods of populating the log files with the information of the successfully transferred objects. We have evaluated the data transfer performance and recovery time overhead of the proposed object-logging-based FT mechanisms on the LADS data transfer framework. Our experimental results reflect that FT mechanisms exhibit negligible overhead (<; 1%) with respect to the data transfer time. Yet, the fault recovery time is 10% higher than the total data transfer time at any fault point.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

PyTorch Implementation of Log-Additive Convolutional Neural Networks

This code is a collection of python code that defines, trains, and tests Log-Additive Convolutional Neural Networks. The model components and training routine are based on the PyTorch python library. The code implements the Log-Additive Convolutional Neural Networks as described in Pagendam et al. 2023. In addition to the Log-Additive Convolutional Neural Networks, this library also defines the Log-Normal Density loss function as described in Pagendam et al. 2023. Code from this paper is not publicly available, so the Pytorch implementation of this type of model is unique to this library.

Callis, Skylar↗
DOE OSTI2022

Using Splunk® Enterprise Search Commands for Advanced Analysis of Ivanti Connect Secure© Logs

Analyzing the logs of even the smallest Information Technology (IT) system can be a challenge considering they can generate millions of lines of log data in a very short time. Splunk® Enterprise is an industry leading tool that allows analysis of log data, which can enhance troubleshooting capabilities, improve system performance, and improve the security posture of an IT system. Ivanti Connect Secure© (ICS) is a market-leading platform powered by the Ivanti Secure Socket Layer Virtual Private Network (SSL VPN) appliance, providing an architecture for secure access to and protection of network resources. This paper describes an approach for using Splunk Enterprise search capabilities to perform advanced data analysis of ICS logs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Well-Log Derived Geomechanical Analysis of Microseismicity in the Mt. Simon Saline Aquifers (Illinois Basin - Decatur Project)

The Illinois Basin Decatur Project (IBDP) successfully demonstrated the safe geologic storage of carbon dioxide at a commercial scale. Within the IBDP project three deep wells (injection (CCS1), monitoring (VW1), geophysical (GM1)) were competed and geophysical logs were recorded. During injection and post-injection periods microseismic monitoring was conducted to create a miscoseismic catalog. The correlations between microseimic attributes and geomechanical well logs define major geomechanical drivers of microseismic expression to understand a reservoir response to CO2 injection in geological context. Utilizing standard sonic and density well logs, the dynamic elastic moduli were calculated and employed to correlate with microseismic pseudo-logs. A multi-dimensional Mu-rho and Lambda-rho (MRLR) hyperdimensional plots display of meaningful data and uncovered subtle relationships between elastic properties of sandstones and the seismological attributes of recorded microseismicity.

Myshakin, Evgeniy↗
NASA NTRS1981

A complete X-ray sample of the high latitude sky from HEAO-1 A-2: log N lo S and luminosity functions

An experiment was performed in which a complete X-ray survey of the 8.2 steradians of the sky at galactic latitudes where the absolute value of b is 20 deg down to a limiting sensitivity of 3.1 x ten to the minus 11th power ergs/sq cm sec in the 2-10 keV band. Of the 85 detected sources 17 were identified with galactic objects, 61 were identified with extragalactic objects, and 7 remain unidentified. The log N - log S relation for the non-galactic objects is well fit by the Euclidean relationship. The X-ray spectra of these objects were used to construct log N - log S in physical units. The complete sample of identified sources was used to construct X-ray luminosity functions, using the absolute maximum likelihood method, for clusters galaxies and active galactic nuclei.

Piccinotti, G.↗
NASA NTRS1991

Solar-A reformatted data files and observing log

An overview is presented of the Solar-A telemetry data files which are to be created and the format and organization which the files are to use. The organization chosen is to be efficient in space, to facilitate access to the data, and to allow the data to be transportable to different machines. An observing log file is to be created automatically, using the reformatted data files as the input. It will be possible to perform searches with the observing log to list cases where instruments are in certain modes and/or seeing certain signal levels. A user will be able to search the observing log and obtain a list of all cases where a given set of conditions are satisfied. An event log will be created listing the times when the instrument or spacecraft modes change.

Morrison, M. D.↗
NASA NTRS1993

Optimal message log reclamation for independent checkpointing

Independent (uncoordinated) check pointing for parallel and distributed systems allows maximum process autonomy but suffers from possible domino effects and the associated storage space overhead for maintaining multiple checkpoints and message logs. In most research on check pointing and recovery, it was assumed that only the checkpoints and message logs older than the global recovery line can be discarded. It is shown how recovery line transformation and decomposition can be applied to the problem of efficiently identifying all discardable message logs, thereby achieving optimal garbage collection. Communication trace-driven simulation for several parallel programs is used to show the benefits of the proposed algorithm for message log reclamation.

Wang, Yi-Min↗