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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Water Level Data from Wells PLM1 and PLM6 for the East River Watershed, Colorado

This dataset (Williams et al., 2020) contains the original un-QA/QC-ed water level data for PLM1 and PLM6 and has been obsoleted. The data contained within this dataset is not to be used. Refer to Faybishenko et al., 2022 (DOI: 10.15485/1866836) for the latest QA/QC-ed data available via ESS-DIVE.This data set contains water level data for the PLM1 and PLM6 wells. PLM1 and PLM6 are location identifiers used by the Watershed Function SFA project for two groundwater monitoring wells along an elevation gradient located along the lower montane life zone of a hillslope near the Pumphouse location. These wells used to monitor subsurface water and carbon inventories and fluxes at the East River Watershed, Colorado, USA. Complete metadata information on the PLM1 and PLM6 wells are available in the related data package reference Varadharajan C, et al (2020). https://doi.org/10.15485/1660962.Data are reported in .csv files per well. The latitude and longitude of each location are given in a file called locations.csv. These data are used for determining the seasonally dependent flow of groundwater under the PLM hillslope. The downslope flow of groundwater in combination with data on groundwater chemistry can be used to estimate rates of solute export from the hillslope to the floodplain and river.These data products are part of the Watershed Function Scientific Focus Area collection effort to further scientific understanding of biogeochemical dynamics from genome to watershed scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Utah FORGE: Fiber Optic Cumulative Strain Change and Strain Change Rate Data From Well 16A Stimulation at Well 16B

This dataset includes Rayleigh Frequency Shift (RFS) Distributed Strain Sensing (DSS) cumulative strain change and change rate data. The data was acquired during the stimulation of Utah FORGE Well 16A(78)-32 in April 2024 via fiber installed in Well 16B(78)-32. The fiber optic data was acquired using Neubrex SR7000 RFS DSS Distributed Strain sensing instruments and is saved here in the format of HDF5 files (.h5 extension). The spatial sampling on the full wellbore profiles is 0.20 centimeters. The data is the far field strain change response from a baseline profile made down the 16B well on April 3, 2024, so each strain value represents the strain change or strain change rate at each depth relative to the baseline reference profile. The data arrays for each type share the same dimensions (number of channels and time stamps).

15 GEOTHERMAL ENERGY↗
DOE OSTI2015

Estimation of anisotropy parameters in organic-rich shale: Rock physics forward modeling approach

Anisotropy analysis becomes an important step in processing and interpretation of seismic data. One of the most important things in anisotropy analysis is anisotropy parameter estimation which can be estimated using well data, core data or seismic data. In seismic data, anisotropy parameter calculation is generally based on velocity moveout analysis. However, the accuracy depends on data quality, available offset, and velocity moveout picking. Anisotropy estimation using seismic data is needed to obtain wide coverage of particular layer anisotropy. In anisotropic reservoir, analysis of anisotropy parameters also helps us to better understand the reservoir characteristics. Anisotropy parameters, especially ε, are related to rock property and lithology determination. Current research aims to estimate anisotropy parameter from seismic data and integrate well data with case study in potential shale gas reservoir. Due to complexity in organic-rich shale reservoir, extensive study from different disciplines is needed to understand the reservoir. Shale itself has intrinsic anisotropy caused by lamination of their formed minerals. In order to link rock physic with seismic response, it is necessary to build forward modeling in organic-rich shale. This paper focuses on studying relationship between reservoir properties such as clay content, porosity and total organic content with anisotropy. Organic content which defines prospectivity of shale gas can be considered as solid background or solid inclusion or both. From the forward modeling result, it is shown that organic matter presence increases anisotropy in shale. The relationships between total organic content and other seismic properties such as acoustic impedance and Vp/Vs are also presented.

58 GEOSCIENCES↗
DOE OSTI2018

The Effects of Stochastic Velocity Variations on Estimating Time Dependent Seismic Moment Tensors: Applications to the Blue Mountain Well Perforation Data

Resolving the time dependent terms in the seismic moment tensor provides important information that can be used to interpret the source process of an explosion, including the separation of isotropic explosion terms from shear forces and potentially isolated force couples. In this report, we detail our method of inverting three component seismic data for the seismic moment tensor. We review possible seismic source models from the simplest isotropic explosion type source to those incorporating the six independent moment tensor terms. The inversion we describe is formulated in the frequency domain, and results in estimates of time dependent moment tensor components. The inversion relies on an accurate estimate of the Green's functions of the Earth. However, given the complexity of the Earth, we explore the effects of inaccuracies in the presumed Earth model used to estimate the Green's functions needed for the inversion. Specifically, we explore the effects of stochastic variations in the Earth models on the inversion results. These tests are synthetic throughout, and show that adding stochastic density/velocity heterogeneity in the presumed Earth model results in reduced amplitude seismic moment tensor estimates, as well as degrading the data misfit. We suggest two mitigation strategies. First, produce a suite of Green's functions using different realizations of the stochastic field within the Earth Model. Secondly, perform the in- version in the power spectral domain, eliminating all phase information. Finally, we analyze actual seismic data collected in winter 2017/2018. The seismic data was collected at in active geothermal well site outside of Winnimucca, NV, and was produced during well stimulation operations. In general, the inversion results were poor, with a high degree of data misfit. We hypothesize that the poor results are a function of a poorly constrained Earth model as well as noisy, high-frequency data being used in the inversion.

58 GEOSCIENCES↗
DOE OSTI2018

Thermal Contact Conductance Modeling

Elastic, plastic and elastic-plastic thermal conductance models were run for Al-6061 and SS-304, respectively. Thermal conductance models were compared to experimental data of samples being pressed together as a function of contact pressure. For SS-304, Elastic and Plastic models did not fit the experimental data well. The Elastic-Plastic model fit the data well and it was shown that surface roughness did not play a major role. For Al-6061, the Elastic model worked best and the models strongly suggested that surface roughness Al plays an important role in how well a thermal conductance model can predict conductance through two pieces of Al in contact with one another.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Applying 3D Geologic Modeling Workflows to the Argillite Reference Case (Rev. 1)

The objective of this short report is to document the application of our 3D geologic modeling workflow to an argillite (shale) host rock. Over the past four years, our team at Los Alamos National Laboratory has developed a geologic modeling workflow that can be applied to generic alluvial basins such as those found in the western United States. In “frontier” or “exploratory” basins where data are sparse, the first steps are to collect, evaluate and integrate available subsurface data into conceptual geologic models. Those models form the basis for constructing the geologic framework model, a 3D geocellular model ideally constrained by seismic and borehole data. To date we have constructed our models using “synthetic” well data derived from conceptual models, without the prospect of validating our workflow using “real” subsurface data. We were tasked to investigate whether our workflow designed for alluvial basin sediments could be applied to other potential repository host rocks. This task also provided the opportunity to work with high-quality subsurface data collected specifically for siting and evaluating a nuclear waste repository. Nagra, the Swiss governmental agency responsible for the disposal of the nation’s radioactive waste, generously provided us with data from two deep boreholes drilled through their argillaceous target formation. The aim of our proof-of-concept demonstration is to evaluate whether geostatistical methods offer a viable approach to property modeling in argillaceous rocks. Nagra provided us with the well data on the condition that we maintain confidentiality with all transferred information and results. Fortunately, Nagra posts numerous technical reports on its public website that describe the subsurface geology in great detail. All of the information and illustrations in this report related to the Swiss repository enterprise are taken from the Nagra public website.

58 GEOSCIENCES↗
DOE OSTI2021

EGS Collab Experiment 2: Testbed Well Orientation Data

This dataset contains the north seeking gyro data for each of the 11 boreholes drilled at the Experiment 2 testbed on the 4100 foot level of the SURF (Sanford Underground Research Facility). Each gyro file is in individual folders for each well. A single folder called 'E2 All Well Trajectories' contains the trajectories for all the wells but in the mine coordinate system. An image of the well layout is provided in a PowerPoint.

15 GEOTHERMAL ENERGY↗
DOE OSTI2019

Sedimentary Geothermal Feasibility Colorado Well Database

Well data were mined from Geothermal Prospector (GTP), Southern Methodist University (SMU), the Colorado Oil and Gas Conservation Commission (COGCC), and the Colorado Geological Survey (CGS). The well data gathered was then used to assess sedimentary geothermal feasibility in the Denver Basin, the Piceance Basin, and the Raton Basin.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Continuous-variable quantum Boltzmann machine

Here, we propose a continuous-variable quantum Boltzmann machine (CVQBM) using a powerful energy-based neural network. It can be realized experimentally on a continuous-variable (CV) photonic quantum computer. We used a CV quantum imaginary time evolution (QITE) algorithm to prepare the essential thermal state and then designed the CVQBM to proficiently generate continuous probability distributions. We applied our method to both classical and quantum data. Using real-world classical data, such as synthetic-aperture radar (SAR) images, we generated probability distributions. For quantum data, we used the output of CV quantum circuits. We obtained high fidelity and low Kullback–Leibler (KL) divergence showing that our CVQBM learns distributions from given data well and generates data sampling from that distribution efficiently. We also discussed the experimental feasibility of our proposed CVQBM. Our method can be applied to a wide range of real-world problems by choosing an appropriate target distribution (corresponding to, e.g., SAR images, medical images, and risk management in finance). Moreover, our CVQBM is versatile and could be programmed to perform tasks beyond generation, such as anomaly detection.

SAR images↗
DOE OSTI2024

Generative network-based approaches to generate stochastic realizations

Generative Adversarial Network (GAN) – based models have been successfully applied in generating different geological models in the literature. However, it is still challenging to use GAN to generate geological realizations with extremely sparse conditioning data (e.g. several well data), which may be regarded as local noise by GAN during the training process. In this work, we propose a novel conditional Generative Adversarial Neural Operator (cGANO) to tackle this challenge. In cGANO, the mapping between conditioning data and output is established through the U-shaped neural operators (UNO), which better preserves local information. Another advantage of using UNO comes from its grid-independent property, which makes the generation of downscaling stochastic geologic realizations possible. We tested the model performance on the IBDP geostatistical dataset with 100 realizations.

58 GEOSCIENCES↗
DOE OSTI2017

Double-peaked Emission Lines Due to a Radio Outflow in KISSR 1219

We present the results from 1.5 and 5 GHz phase-referenced VLBA and 1.5 GHz Karl G. Jansky Very Large Array (VLA) observations of the Seyfert 2 galaxy KISSR 1219, which exhibits double-peaked emission lines in its optical spectrum. The VLA and VLBA data reveal a one-sided core-jet structure at roughly the same position angles, providing evidence of an active galactic nucleus outflow. The absence of dual parsec-scale radio cores puts the binary black-hole picture in doubt for the case of KISSR 1219. The high brightness temperatures of the parsec-scale core and jet components (>10{sup 6} K) are consistent with this interpretation. Doppler boosting with jet speeds of ≳0.55 c to ≳0.25 c , going from parsec to kiloparsec scales, at a jet inclination ≳50° can explain the jet one-sidedness in this Seyfert 2 galaxy. A blueshifted broad emission line component in [O iii] is also indicative of an outflow in the emission line gas at a velocity of ∼350 km s{sup −1}, while the [O i] doublet lines suggest the presence of shock-heated gas. A detailed line ratio study using the MAPPINGS III code further suggests that a shock+precursor model can explain the line ionization data well. Overall, our data suggest that the radio outflow in KISSR 1219 is pushing the emission line clouds, both ahead of the jet and in a lateral direction, giving rise to the double peak emission line spectra.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1971

Simultaneous measurements of the hydrogen airglow emissions of Lyman alpha, Lyman beta, and Balmer alpha.

Comparison of Lyman-alpha, 740- to 1050-A, and Balmer-alpha airglow measurements made at 134 deg solar-zenith angle on Oct. 13, 1969, with resonance-scattering models of solar radiation. Model comparison with Lyman-alpha data fixes the hydrogen column abundance over 215 km to 2 x 10 to the 13th per cu cm within a factor of 2. Differences between the Lyman-alpha model and data indicate a polar-equatorial departure from spherical symmetry in the hydrogen distribution. A Lyman-beta model based on the hydrogen distribution found to fit the Lyman-alpha data fits the spatial variation of the 740- to 1050-A data well from 100 to 130 km, but it does not fit the data well at higher altitudes; thus the presence of more rapidly absorbed shorter-wavelength radiation is indicated. This same resonance-scattering model yields Balmer-alpha intensities that result in good spatial agreement with the Balmer-alpha measurements, but a fivefold increase in the measured solar line center Lyman-beta flux is required (as required for the Lyman-beta measurement). The intensity ratio of Lyman-beta and Balmer-alpha at night is found to be a simple measure of the hydrogen optical depth if measurements with good accuracy can be made in the visible and ultraviolet spectrum.

Weller, C. S.↗
DOE OSTI2016

Utah FORGE: Groundwater Data

This submission includes two modeled drawdown scenarios with new supply well locations, a total dissolved solids (TDS) concentration grid (raster dataset representing the spatial distribution of TDS), and an excel spreadsheet containing well data.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Geophysical borehole logging data of wells ER-GLS1, ER-GUM1, ER-PLM7, and ER-PLM8 at the East River Watershed, Colorado

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.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Bayesian seismic inversion based on rock-physics prior modeling for the joint estimation of acoustic impedance, porosity and lithofacies

We propose a Bayesian approach for seismic inversion to estimate acoustic impedance, porosity and lithofacies within the reservoir conditioned to post-stack seismic and well data. The link between elastic and petrophysical properties is given by a joint prior distribution for the logarithm of impedance and porosity, based on a rock-physics model. The well conditioning is performed through a background model obtained by well log interpolation. Two different approaches are presented: in the first approach, the prior is defined by a single Gaussian distribution, whereas in the second approach it is defined by a Gaussian mixture to represent the well data multimodal distribution and link the Gaussian components to different geological lithofacies. The forward model is based on a linearized convolutional model. For the single Gaussian case, we obtain an analytical expression for the posterior distribution, resulting in a fast algorithm to compute the solution of the inverse problem, i.e. the posterior distribution of acoustic impedance and porosity as well as the facies probability given the observed data. For the Gaussian mixture prior, it is not possible to obtain the distributions analytically, hence we propose a Gibbs algorithm to perform the posterior sampling and obtain several reservoir model realizations, allowing an uncertainty analysis of the estimated properties and lithofacies. Both methodologies are applied to a real seismic dataset with three wells to obtain 3D models of acoustic impedance, porosity and lithofacies. The methodologies are validated through a blind well test and compared to a standard Bayesian inversion approach. Using the probability of the reservoir lithofacies, we also compute a 3D isosurface probability model of the main oil reservoir in the studied field.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Deploying a Publicly Available and Living National Oil and Gas Well Geodatabase: CO2-Locate: A Dynamic Database & Tool

Emission mitigation and safe geologic carbon storage require an understanding of local wellbore infrastructure, yet well data are siloed across many entities. Addressing this challenge, the National Energy Technology Laboratory published CO2-Locate, an integrated and dynamic national wellbore geodatabase. CO2-Locate offers up to date well data spanning more than 40 federal, state, and tribal entities, as well as spatially summarized insights designed to support commercial, regulatory, and research communities as they strive to curb climate change through a national energy transition.

Pfander, Isabelle↗
DOE OSTI2023

Utah FORGE: Powder X-ray Diffraction Data from Well 16A(78)-32 Core

This dataset from Lawrence Livermore National Laboratory (LLNL) consists of four raw X-ray diffraction (XRD) scans and preliminary results of quantitative XRD analysis. The scanned samples were prepared from four subcores, which came from various depths of the FORGE well 16A(78)-32 core. Desired core lengths were selected from available core photos (on GDR), provided by FORGE personnel, and subcored at LLNL. The XRD scans were collected in May 2023 at LLNL as pre-experimental characterization data for these subcores, which will be used in core-flooding experiments at LLNL and in triaxial direct shear experiments at Los Alamos National Laboratory as part of DOE Project 5-2428. XRD scans are in RAW file format (e.g., FORGE-5477-full.raw) and are suitable for viewing and analysis using open-source quantitative XRD software (e.g., Profex; www.profex-xrd.org ) and/or other proprietary instrument software.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Association Between Injection and Microseismicity in Geothermal Fields With Multiple Wells: Data-Driven Modeling of Rotokawa, New Zealand, and Húsmúli, Iceland

Understanding injection-induced microseismicity in geothermal systems can provide insight into reservoir connectedness. However, fault and reservoir complexity are difficult to represent in simple analytical models, which makes it difficult to discern clear relationships from incidental associations. Here, we have used data-driven models to study how fluid injection and microseismicity are related in the Rotokawa (New Zealand) and Hellisheiði (Iceland) geothermal fields. We tested two classes of model: (a) lagged linear regression of seismicity rate as a function of well injection rates; and (b) systematic extraction of injection time series features that are then evaluated for associations with the seismicity. These models allowed us to determine which wells had the greatest correlation with microseismicity and to explain this association in a reservoir context. Finally, exploring different data types and transformations, we were unable to establish a link between rapid changes in injection rate and seismicity spikes, as suggested by some theoretical models.

15 GEOTHERMAL ENERGY↗