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The zip file below contains daily reports produced during the spring 2024 stimulation of wells 16A/16B(78)-32 at Utah FORGE . These reports cover a period from March 29th to April 28th, 2024. They discuss the activity on the rig each day, with each report containing information on general operations, management, safety, and weather.
These are the daily reports created during and around the August 2024 circulation test done on wells 16A(78)-32 and 16B(78)-32 at the Utah FORGE site. Information about the rig activity during this period includes details on rig up, depths, pumping, water temperature, safety, and cleanout. The reports span all rig activities from July 29th, 2024 through September 5th, 2024.
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This drilling data for Utah FORGE well 16B(78)-32 include a well survey, core summary, mud and mud temperature logs, daily reports of the drilling process, and additional data from the Pason oil and gas company. 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 proposed total depth was 10,658 feet, which was exceeded. Spudding began on April 26th, 2023. As of June 20th, 2023, the total depth measured 10,947 feet and the vertical depth measured 8,357 feet. Drilling included the trial use of insulated drill pipe (IDP) from Eavor Technologies, which was considered a success. IDP restricts counter-current heat transfer between drilling fluid inside the drill string and hotter returning fluid in the annulus, thereby ensuring the bottomhole assembly (BHA) remains submerged in cool fluid. Eavor rented 350 joints of IDP to FORGE which were run in two consecutive BHAs at the well. The results of this trial are included here.
To characterize Coronavirus Disease 2019 (COVID-19) transmission dynamics in each of the metropolitan statistical areas (MSAs) surrounding Dallas, Houston, New York City, and Phoenix in 2020 and 2021, we extended a previously reported compartmental model accounting for effects of multiple distinct periods of non-pharmaceutical interventions by adding consideration of vaccination and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants Alpha (lineage B.1.1.7) and Delta (lineage B.1.617.2). For each MSA, we found region-specific parameterizations of the model using daily reports of new COVID-19 cases available from January 21, 2020 to October 31, 2021. In the process, we obtained estimates of the relative infectiousness of Alpha and Delta as well as their takeoff times in each MSA (the times at which sustained transmission began). The estimated infectiousness of Alpha ranged from 1.1x to 1.4x that of viral strains circulating in 2020 and early 2021. The estimated relative infectiousness of Delta was higher in all cases, ranging from 1.6x to 2.1x. The estimated Alpha takeoff times ranged from February 1 to February 28, 2021. The estimated Delta takeoff times ranged from June 2 to June 26, 2021. In conclusion, estimated takeoff times are consistent with genomic surveillance data.
During an early period of the Coronavirus Disease 2019 (COVID-19) pandemic, the Navajo Nation, much like New York City, experienced a relatively high rate of disease transmission. Yet, between January and October 2020, it experienced only a single period of growth in new COVID-19 cases, which ended when cases peaked in May 2020. The daily number of new cases slowly decayed in the summer of 2020 until late September 2020. In contrast, the surrounding states of Arizona, Colorado, New Mexico, and Utah all experienced at least two periods of growth in the same time frame, with second surges beginning in late May to early June. Here, we investigated these differences in disease transmission dynamics with the objective of quantifying the contributions of non-pharmaceutical interventions (NPIs) (e.g., behaviors that limit disease transmission). We considered a compartmental model accounting for distinct periods of NPIs to analyze the epidemic in each of the five regions. We used Bayesian inference to estimate region-specific model parameters from regional surveillance data (daily reports of new COVID-19 cases) and to quantify uncertainty in parameter estimates and model predictions. Our results suggest that NPIs in the Navajo Nation were sustained over the period of interest, whereas in the surrounding states, NPIs were relaxed, which allowed for subsequent surges in cases. Our region-specific model parameterizations allow us to quantify the impacts of NPIs on disease incidence in the regions of interest.
In this chapter, we provide notes on the state of metadata collection and organization practices and reflect on the achievements of the MetaCO task group. We also include an utility perspective on how to use this report in daily data management practice and a short guide is provided to show how metadata practices can be initiated. The chapter, and the report, is concluded within an outlook, sketching future progress and opportunities on the horizon.
We provide daily stable isotope (2H & 18O) ratios in soil water and xylem (plant stem) water, as well as the sap flow (transpiration) and the soil's matric potential at a forested site near Gothic, Colorado, in the East River catchment. We measured the stable isotopic composition of the transpiration and the daily transpiration flux sum of three aspen and three engelmann spruce. In both forest stands, we installed a soil profile and measured the soil matric potential at 15, 30, and 60 cm depth as well as the stable isotopes of soil pore water at 5, 10, 30, 60, and 90 cm depths. All isotope measurements were done in situ via vapor probes connected to a cavity ring down spectrometer (Picarro L1240i).We further report the daily meteorological data observed at billy barr near our study site. We also provide for each tree the relative share of root water uptake derived from the isotope measurements via a Bayesian mixing model (MixSIAR).The daily data is provided as a time series in "Iso_MP_Sap_DataDaily_ESSDiveUpload.csv" and the units are provided in "dd.csv"; the location of the instrumented trees and soil profiles are given as latitude and longitude coordinates saved as CSV and KMZ files; and a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata.The data was gathered to investigate the short-term changes of the water sources (i.e., variation of root water uptake from different soil depths) of the studied subalpine trees.Update 07/16/2025: The relative and absolute plant water uptake depths were grouped to ensure that the MixSIAR model was applied with endmembers that differed in their d2H value by at least 3 permill and at least 1 permill for d18O. Whenever the difference between observed d2H values for two or more probes at neighboring depths was less than 3 permill, we used the average value for the source water endmember. For days at which probes that were not next to each other measurements did not differ at least 3 permill, the average of all probes between these two depths was used as the water source endmember representing the depth range between these two probes.
As part of DEEPEN (DE-risking Exploration of geothermal Plays in magmatic ENvironments), a 3D play fairway analysis (PFA) was conducted at Newberry Volcano in Central Oregon for multiple play types (conventional hydrothermal, superhot EGS, and supercritical). For use in this PFA, combined full tensor broadband magnetotelluric (MT) and gravity data were acquired, processed, and inverted by Enthalpion Energy LLC (Enthalpion) with support from NREL staff. The data collection efforts took place from June 19th to July 24. Data were collected with the goal of gaining an improved understanding of the South Flank and the extent of the magma chamber. This GDR submission includes the raw data, single inversions, joint inversions, resolution matrices, and a report on these processes. More detailed information about the folder structure of the datasets included in this submission may be found on pages 94-97 of Magnetotelluric and Gravity Survey Report.pdf below.
Chronic pain is a common problem, with more than one-fifth of adult Americans reporting pain daily or on most days. It adversely affects the quality of life and imposes substantial personal and economic costs. Efforts to treat chronic pain using opioids had a central role in precipitating the opioid crisis. Despite an estimated heritability of 25–50%, the genetic architecture of chronic pain is not well-characterized, in part because studies have largely been limited to samples of European ancestry. To help address this knowledge gap, we conducted a cross-ancestry meta-analysis of pain intensity in 598,339 participants in the Million Veteran Program, which identified 126 independent genetic loci, 69 of which are new. Pain intensity was genetically correlated with other pain phenotypes, level of substance use and substance use disorders, other psychiatric traits, education level and cognitive traits. Integration of the genome-wide association studies findings with functional genomics data shows enrichment for putatively causal genes (n = 142) and proteins (n = 14) expressed in brain tissues, specifically in GABAergic neurons. Drug repurposing analysis identified anticonvulsants, β-blockers and calcium-channel blockers, among other drug groups, as having potential analgesic effects. Our results provide insights into key molecular contributors to the experience of pain and highlight attractive drug targets.
Abstract Changes in precipitation patterns with climate change could have important impacts on human and natural systems. Zhang et al. (2021, https://doi.org/10.1029/2020gl092293 ) report trends in daily precipitation patterns over the last five decades in the western United States, focusing on meteorological drought. They report that dry intervals (calculated at the annual or seasonal level) have increased across much of the southwestern U.S., with statistical assessment suggesting the results are statistically robust. However, Zhang et al. (2021, https://doi.org/10.1029/2020gl092293 ) preprocess their annual (or seasonal) averages to compute 5‐year moving window averages before using established statistical techniques for trend analysis that assume independence about some fixed trend. Here we show that the moving window preprocessing violates that independence assumption and inflates the statistical significance of their trend estimates. This raises questions about the robustness of their results. We conclude by discussing the difficulty of adjusting for spatial structure when assessing time trends in a regional context.
This dataset provides daily estimates of evapotranspiration (ET) and the transpiration-to-evapotranspiration ratio (T/ET) across 47 terrestrial National Ecological Observatory Network (NEON) sites spanning diverse environmental and biome conditions in the United States across three years of data (2019-2021). Daily ET is reported in both energy units (MJ m⁻² day⁻¹) and equivalent water depth (mm day⁻¹), assuming a constant latent heat of vaporization of 2.45 MJ/kg. The primary method uses a hybrid recurrent neural network–Penman–Monteith framework (RNN-PM), which integrates physically based surface energy balance constraints with data-driven learning to partition ET into transpiration and evaporation components. Model inputs include in situ meteorological observations (air temperature, vapor pressure deficit, wind speed, and radiation) combined with satellite-derived land surface temperature, leaf area index, and soil moisture. For benchmarking and uncertainty assessment, T/ET estimates from seven additional models are included: Priestley-Taylor Jet Propulsion Laboratory (PT-JPL), Penman-Monteith (P-M), Two-Source Energy Balance (TSEB), Support Vector Regression (SVR), and Categorical Boosting (CatBoost), among others—spanning empirical, machine-learning, and process-based approaches (see methods section or linked publication for detailed descriptions). Data Package Contents: The dataset a csv files containing daily ET and T/ET estimates for each site and model, along with associated metadata files these variables. Data can be accessed using common spreadsheet software (e.g., Microsoft Excel, LibreOffice) or programming environments such as R or Python. Together, these data support cross-site comparisons of ecosystem water use, evaluation of ET partitioning methods, and development of improved land–atmosphere exchange models.
Inconel® Alloy 740H® (alloy 740H) was the first age-hardenable nickel-based alloy approved by the ASME Boiler & Pressure Vessel Code for use in pressure-boundary applications. Over the past ~20 years the alloy has been optimized for weldability and high-temperature stability, approved for use in different applications. Development of a supply chain combined with the advantageous properties of the alloy (high-temperature creep strength, oxidation and corrosion resistance, etc.) have resulted in the alloy being applied to new high-temperature power cycle demonstration projects, and of particular interest are applications to concentrating solar power (CSP) to enable higher-efficiency Generation 3 CSP systems and the corresponding supercritical CO 2 (sCO 2 ) power cycle components (heat exchangers, piping, etc.). The high allowable stresses of alloy 740H also make it a desirable material for current Generation 2 CSP solar power receivers to improve cyclic capability and/or reduce receiver height. Recent experiences in demonstration projects utilizing alloy 740H identified cracking issues during welding and fabrication. In this project, a detailed study was done to confirm and clarify the Stress Relaxation Cracking (SRxC) mechanism, also known as stress relief cracking or strain-age cracking (SAC), during post-weld heat-treatment (PWHT). This involved detailed microscopy and advanced characterization to understand the root cause(s) of three failures obtained from industry. Based in-part on these findings, a targeted laboratory based SRxC test method was utilized to evaluate variables such as heat-to-heat variations, strain level, PWHT temperature, and starting material condition on three heats of alloy 740H. Industrial shop welding of cold-worked plates was also conducted. The research showed the following: SRxC was confirmed as the cracking mechanism for all field failures; Stress state (from residual stresses, constraint, deformation, and local stress concentrations) was playing a significant role in field failures and laboratory testing confirmed increasing susceptibility for all heats with increasing strain levels. High levels of microstructural strain were identified at crack initiation locations, in some cases leading to local recrystallization; Precipitate free zones (PFZs) at grain boundaries were found at relaxation cracks and crack initiation locations uniquely associated with SRxC in alloy 740H. Laboratory testing reproduced this microstructural feature which had only previously been reported in long-term creep testing of weldments. Advanced nano-scale characterization confirmed the presence of a moving boundary leading to coarsening of precipitates and PFZs where damage accumulated; The research suggested heat-to-heat variations due to local chemistry and processing may influence SRxC susceptibility, but more work is needed to fully clarify these effects. To disseminate the key learnings from this research to the scientific and engineering communities and alloy 740H end users, multiple technical publications and presentations were made, an industrial alloy 740H users meeting was held, and a new industry guideline specification document which can be directly implemented by end-users of alloy 740H was produced.
Soil CO 2 efflux is commonly measured monthly or seasonally, leaving daily dynamics poorly resolved and contributing to global estimation uncertainty. We trained a single Long Short-Term Memory (LSTM) model to predict daily soil CO 2 efflux across 82 globally distributed sites in COSORE, with 0.2%–46.9% daily data coverage from 2003 to 2020. Despite using far fewer sites than are typically used to train a single deep learning model, with observations biased toward temperate mesic sites, the LSTM model performed well at approximately one-third of sites, reconstructed nearly 2 decades of daily efflux, and outperformed commonly used approaches for estimating daily efflux when applied to the same data set. Performance was weakest at pronounced peaks and troughs and at non-temperate sites with <1.5 years of observations and irregular data patterns. Nevertheless, annual efflux from reconstructed daily data had <40% error even at underperforming sites, substantially improving estimates derived from monthly and seasonal sampling (maximum errors of 95% and 136%, respectively). Temperature sensitivity (Q 10 ) estimated from reconstructed daily predictions closely matched estimates from daily observations, whereas Q 10 values derived from monthly or seasonal observations deviated substantially, suggesting that coarse temporal sampling may contribute to uncertainty in reported Q 10 values. Consistent daily reconstructions further enabled trend analyses for well-performing, predominantly temperate sites and showed increasing soil CO 2 efflux at most sites from 2003 to 2020, with more variable summer trends. Despite limitations, these results demonstrate the potential of LSTM models to reconstruct daily soil CO 2 efflux and reduce estimation uncertainties from sparse observations.
The Glen Canyon Dam (GCD) is a Colorado River Storage Project (CRSP) power resource that is a component of the Salt Lake City Area Integrated Projects (SLCA/IP). The 2016 record of decision (ROD) for the GCD long-term experimental and management plan (LTEMP) final Environmental Impact Statement (EIS) specifies criteria for GCD monthly water releases, daily and hourly operating limits, and experimental releases. This report presents a financial analysis of the Smallmouth bass (Micropterus dolomieu) (SMB) flows implemented at GCD during Water Year (WY) 2024. These bypass flows were introduced by the U.S. Bureau of Reclamation (USBR) as an emergency response to the growing threat posed by invasive SMB in the Colorado River ecosystem downstream of the dam. SMB are a non-native predatory species that pose a significant threat to native fish populations, including the endangered humpback chub (Gila cypha). The thermal regime below GCD, typically cold due to hypolimnetic releases from Lake Powell, has historically served as a thermal barrier limiting SMB establishment. However, persistently low reservoir levels in recent years have reduced stratification in Lake Powell, allowing warmer water to be released downstream. This has enabled SMB to spawn successfully below the dam, prompting urgent ecological concerns. To mitigate the risk of SMB proliferation, the USBR implemented a series of bypass flows in WY 2024. Drawn from a lower elevation than the penstocks, the bypass structures released cooler water downstream. These short-duration bypass flows aimed to keep temperatures cool enough to prevent SMB from spawning, thereby reducing the ecological threat posed by this invasive species. Although motivated by ecological objectives, these bypass flows came with financial tradeoffs. Releasing water through the bypass structures instead of the turbines at GCD reduced hydropower generation, resulting in a significantly lower financial position for Western Area Power Administration (WAPA), which is responsible for marketing the electricity produced by the GCD Powerplant. This report analyzes the financial impact of the SMB flows implemented from July to November 2024. These experimental releases led to an estimated financial cost of approximately $18.9 million, primarily driven by the substantial volume of water diverted through the bypass structures. This study applies an integrated set of tools to estimate WAPA financial impacts by simulating GCD under two types of cases; namely, (1) a “With Experiment” case that mimics the water operations that actually occurred, including the SMB bypass flows, and (2) a “Without Experiment” case that simulates operations under the assumption that the SMB flows did not occur. Both cases comply with LTEMP hourly and daily operating criteria, and the monthly water release volumes are assumed to be identical under both cases. The Colorado River Storage Project Python-based model (CRiSPPy) model was the main modeling tool used to simulate the dispatch of the GCD hydropower plant and associated water releases from Lake Powell. In the modeling process, the research team used extensive data sets and historical information on SLCA/IP power plant characteristics, hydrologic conditions, and WAPA’s power purchases and sales prices.
The Glen Canyon Dam (GCD) is a Colorado River Storage Project (CRSP) power resource that is a component of the Salt Lake City Area Integrated Projects (SLCA/IP). The 2016 record of decision (ROD) for the GCD long-term experimental and management plan (LTEMP) final Environmental Impact Statement (EIS) specified criteria for GCD monthly water releases, daily and hourly operating limits, and experimental releases. This report examines the financial implications of the high flow experiment (HFE) conducted at GCD during the spring of Water Year (WY) 2023 as required by the LTEMP HFE Protocol. This report is part of a series of reports that describe the financial costs of LTEMP experimental releases since the 2016 ROD was adopted in January 2017. Previous reports analyzed the impact of several past HFEs and Bug Flow Experiments. This report focuses on the HFE conducted in April 2023. For this experimental release, financial costs of approximately $1.33 million were incurred because the HFE required sustained water releases exceeding the power plant’s maximum turbine flow rate. In addition, during the experiment, operators were not allowed to shape GCD power production, either to follow Firm Electric Service (FES) customer day-ahead energy deliveries or to respond to market prices. This study identifies the main factors contributing to the HFE costs and examines the interdependencies among these factors. It applies an integrated set of tools to estimate Western Area Power Administration (WAPA) financial impacts by simulating GCD under two types of cases; namely, (1) a “With Experiment” case that mimics the operations that actually occurred and (2) a “Without Experiment” case that simulates operations under the assumption that the HFE did not occur. The “With Experiment” case mimics operations during the HFE and the entire month the HFE occurred. It complies with LTEMP hourly and daily operating criteria. The “Without Experiment” case assumes that the HFE did not occur. The monthly water release volume is assumed to be identical under both cases. The Colorado River Storage Project Python-based model (CRiSPPy) model was the main modeling tool used to simulate the dispatch of the GCD hydropower plant and associated water releases from Lake Powell. In the modeling process, the research team used extensive data sets and historical information on SLCA/IP power plant characteristics, hydrologic conditions, and WAPA’s power purchases and sales prices. In addition to estimating the financial impact of the HFE, the team used the CRiSPPy model to gain insights into the interplay among ROD operating criteria, exceptions made to criteria to accommodate the HFE, and WAPA operating practices.
Dark matter (DM) particles with sufficiently large cross sections may scatter as they travel through Earth’s bulk. The corresponding changes in the DM flux give rise to a characteristic daily modulation signal in detectors sensitive to DM-electron interactions. Here, we report results obtained from the first underground operation of the DAMIC-M prototype detector searching for such a signal from DM with MeV-scale mass. A model-independent analysis finds no modulation in the rate of 1 e − events with sidereal period, where a DM signal would appear. We then use these data to place exclusion limits on DM in the mass range [ 0.53 , 2.7 ] MeV / c 2 interacting with electrons via a dark photon mediator. Taking advantage of the time-dependent signal we improve by ∼ 2 orders of magnitude on our previous limit obtained from the total rate of 1 e − events, using the same dataset. This daily modulation search represents the current strongest limit on DM-electron scattering via ultralight mediators for DM masses around 1 MeV / c 2 . Published by the American Physical Society 2024
In this paper, we explore whether the infection-rate of a disease can serve as a robust monitoring variable in epidemiological surveillance algorithms. The infection-rate is dependent on population mixing patterns that do not vary erratically day-to-day; in contrast, daily case-counts used in contemporary surveillance algorithms are corrupted by reporting errors. The technical challenge lies in estimating the latent infection-rate from case-counts. Here we devise a Bayesian method to estimate the infection-rate across multiple adjoining areal units, and then use it, via an anomaly detector, to discern a change in epidemiological dynamics. We extend an existing model for estimating the infection-rate in an areal unit by incorporating a Markov random field model, so that we may estimate infection-rates across multiple areal units, while preserving spatial correlations observed in the epidemiological dynamics. To carry out the high-dimensional Bayesian inverse problem, we develop an implementation of mean-field variational inference specific to the infection model and integrate it with the random field model to incorporate correlations across counties. The method is tested on estimating the COVID-19 infection-rates across all 33 counties in New Mexico using data from the summer of 2020, and then employing them to detect the arrival of the Fall 2020 COVID-19 wave. We perform the detection using a temporal algorithm that is applied county-by-county. We also show how the infection-rate field can be used to cluster counties with similar epidemiological dynamics.