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WLS gathers all known relevant contextual data along with standard event log information, processes it into an easily consumable format for analysis by 3rd party tools, and forwards the logs to any compatible log server.
Technologies including NMR logging apparatus and methods are disclosed. Example NMR logging apparatus may include surface instrumentation and one or more downhole probes configured to fit within an earth borehole. The surface instrumentation may comprise a power amplifier, which may be coupled to the downhole probes via one or more transmission lines, and a controller configured to cause the power amplifier to generate a NMR activating pulse or sequence of pulses. Impedance matching means may be configured to match an output impedance of the power amplifier through a transmission line to a load impedance of a downhole probe. Methods may include deploying the various elements of disclosed NMR logging apparatus and using the apparatus to perform NMR measurements.
Techniques are provided for globally appending data from a group of distributed processes to a shared file using a log-structured file system. Data generated by a plurality of processes in a parallel computing system are appended to a shared file by storing the data to the shared file using a log-structured file system (such as a Parallel Log-Structured File System (PLFS)); and generating an index entry for the data, the index entry comprising a logical offset entry and a timestamp entry indicating a time of the storage, wherein the logical offset entry is resolved at read time. The logical offset entry can be populated with an append placeholder that is resolved when the shared file is read. At read time, a plurality of the index entries associated with the shared file can be sorted using the timestamp entry to deliver the requested shared file to a requesting application.
The Log-N/Period system utilizes a compensated ion chamber to measure neutron radiation in the reactor core. This analysis focuses on determining a more accurate value for the sensitivity of the installed compensated ion chambers for the Log-N/Period system at the ATR. Using calibration procedures a formula for indirectly calculating the sensitivity of the chambers was derived. Using historical data from approximately ten reactor cycles a new average sensitivity for both channels of the Log-N/Period system was determined.
This dataset is a collection of well logs provided by Schlumberger Technologies from the Utah FORGE well 16B(78)-32 drilling project. Information here includes critical borehole information collected by an ultrasonic borehole imager (UBI) and a fullbore formation microimager (FMI). Well 16B(78)-32 serves as the production well for reservoir creation, fluid circulation, and demonstration of heat extraction for the FORGE project. It has been drilled as a doublet approximately 300 feet parallel to and above the injection well 16A(78)-32. The total depth measured 10,947 feet and the vertical depth measured 8,357 feet.
Data, logs, and graphics associated with the drilling and testing of Utah FORGE deep test well 58-32 (MU-ESW1) near Roosevelt Hot Springs.
These are production logging tests (PLT) results from Halliburton collected during the nine hour circulation test performed at Utah FORGE on April 27th, 2024. The circulation test involved injecting into well 16A(78)-32 and producing out of well 16B(78)-32) and was carried out after the fracturing of both of those wells. Data attached here includes raw and processed PLT logs from both wells, as well as technical reports summarizing the data collection and results.
This submission includes drilling logs, mud log, deviation survey, site observation notes, and whole core scans from the Citizen Green #1 well drilled on King Island, CA (API 07720688) as part of the WESTCARB partnership basin characterization effort. These datasets were gathered by JAF but created and compiled by a variety of WESTCARB operations team members and contractors including Sandia Resources, Stratagraphic Corp, John Henry Beyer, Paul Graham Drilling, and Core Labs.
Mud logs for wells 83-11, 68-6, 33A-7, 33A-7RD, 52B-7, and 88-1 at West Flank
This is a compilation of logs and data from Well 82-33 in the Roosevelt Hot Springs area in Utah. This well is also in the Utah FORGE study area. The file is in a compressed .zip format and there is a data inventory table (Excel spreadsheet) in the root folder that is a guide to the data that is accessible in subfolders.
This article discusses experimental approaches for the development of gamma spectroscopy well logging system. Considering the size of borehole sonde, we customize 2 x 2 inches inorganic scintillators and the system including high voltage, preamplifier, amplifier and multichannel analyzer (MCA). The calibration chart is made by test using standard radioactive sources so that the measured count rates are expressed by energy spectrum. Optimum high-voltage supplies and the measurement parameters of each detector are set up by experimental investigation. Also, the responses of scintillation detectors have been examined by analysis according to the distance between source and detector. Because gamma spectroscopy well logging needs broad spectrum, high sensitivity and resolution, the energy resolution and sensitivity as a function of gamma ray energy are investigated by analyzing the gamma ray activities of the radioactive sources.
Molecular-replacement phasing of macromolecular crystal structures is often fast, but if a molecular-replacement solution is not immediately obtained the crystallographer must judge whether to pursue molecular replacement or to attempt experimental phasing as the quickest path to structure solution. The introduction of the expected log-likelihood gain has given the crystallographer a powerful new tool to aid in making this decision. The eLLG is the log-likelihood gain on intensity expected from a correctly placed model. It is calculated as a sum over the reflections of a function dependent on the fraction of the scattering for which the model accounts, the estimated model coordinate error and the measurement errors in the data. It is shown how the eLLG may be used to answer the question 'can I solve my structure by molecular replacement?'. However, this is only the most obvious of the applications of the eLLG. It is also discussed how the eLLG may be used to determine the search order and minimal data requirements for obtaining a molecular-replacement solution using a given model, and for decision making in fragment-based molecular replacement, single-atom molecular replacement and likelihood-guided model pruning.
Deterministic replay of a parallel application is commonly used for discovering bugs or to recover from a hard fault with message-logging fault tolerance. For message passing programs, a major source of overhead during forward execution is recording the order in which messages are sent and received. During replay, this ordering must be used to deterministically reproduce the execution. Previous work in replay algorithms often makes minimal assumptions about the programming model and application in order to maintain generality. However, in many cases, only a partial order must be recorded due to determinism intrinsic in the code, ordering constraints imposed by the execution model, and events that are commutative (their relative execution order during replay does not need to be reproduced exactly). In this paper, we present a novel algebraic framework for reasoning about the minimum dependencies required to represent the partial order for different concurrent orderings and interleavings. By exploiting this theory, we improve on an existing scalable message-logging fault tolerance scheme. The improved scheme scales to 131,072 cores on an IBM BlueGene/P with up to 2x lower overhead than one that records a total order.
Deep learning has recently demonstrated state-of-the art performance on key tasks related to the maintenance of computer systems, such as intrusion detection, denial of service attack detection, hardware and software system failures, and malware detection. In these contexts, model interpretability is vital for administrator and analyst to trust and act on the automated analysis of machine learning models. Deep learning methods have been criticized as black box oracles which allow limited insight into decision factors. In this work we seek to "bridge the gap'' between the impressive performance of deep learning models and the need for interpretable model introspection. To this end we present recurrent neural network (RNN) language models augmented with attention for anomaly detection in system logs. Our methods are generally applicable to any computer system and logging source. By incorporating attention variants into our RNN language models we create opportunities for model introspection and analysis without sacrificing state-of-the art performance. We demonstrate model performance and illustrate model interpretability on an intrusion detection task using the Los Alamos National Laboratory (LANL) cyber security dataset, reporting upward of 0.99 area under the receiver operator characteristic curve despite being trained only on a single day's worth of data.
This is the full wireline geophysical datasets for characterization of the EGS Collab Experiment #1 testbed on the 4850 level. A metadata file is included within the dataset explaining the logs, fracture picks, etc. Eight boreholes were drilled for this testbed and each one was logged with north seeking gyro, optical televiewer, acoustic televiewer, fluid temperature conductivity, resistivity and gamma, and full waveform sonic. In these folders are the processed results as text, csv, and pdf files, along with the raw data which will need to be read using WellCAD software.
Provided here are a final thermal conductivity test report, a drilling log, and a heat rejection log from Carbondale, CO. Also attached is a report made before drilling, which contains predictions on the hydrogeologic conditions of the drill site. The forty-eight (48.9) hour in-situ thermal conductivity test was performed on the 28th through the 30th, of November 2023. The test was performed at the borehole drilled on November 16th through the 20th, at the 3rd Street Center at 520 S. 3rd, Street in Carbondale, Colorado. Testing was done with a certified Ewbank portable test unit.
The purpose of this data set is to provide a means of characterizing the bedrock variability both within a single borehole location and across the East River Watershed. The East River is part of the Watershed Function Scientific Focus Area (WFSFA) located in the Upper Colorado River Basin, United States, and all boreholes are located within Mancos shale bedrock. The data set contains geophysical borehole logging data acquired in October 2018 in boreholes ER-GUM1 (East River- Gothic Upper Montane Borehole #1) and ER-GLS1 (East River- Gothic Lower Subalpine Borehole #1), and acquired in October 2019 in boreholes ER-PLM7 (East River- Pumphouse Lower Montane Borehole #7) and ER-PLM8 (East River- Pumphouse Lower Montane Borehole #8). It contains depth, natural gamma ray emission, spectral gamma ray emission(Th, K, U), magnetic susceptibility, fluid temperature and electrical conductivity, electromagnetic (EM) induction derived rock electrical resistivity, heat pulse and spinner flow measurements to estimate groundwater flow, and Nuclear Magnetic Resonance (NMR) derived water content and hydraulic conductivity. Data for each borehole are provided as comma delimited text files. For each borehole accurate location information is provided (based on RTK GPS (Real Time Kinematic Global Positioning System) surveying). Borehole logging data were acquired using Mount Sopris borehole tools, and Vista Clara Javelin for NMR data.
This dataset contains the raw data sets of thee UW PRB 1 well's geophysical well logs, core measurements and fluid analysis for the CarbonSafe II project at the Dryfork Station in Gillette, WY. Focus was placed on the Lakota, Hulett and Minnelusa Formations and their overlying seals. Also included are well logs from legacy wells from the surrounding area.