Search NASA⌕ Search

SEARCH · Search NASA

Results for “producible well”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records

A Simple Data-Centric Methodology for Producible Geothermal Well Determinations: Preprint

The Bureau of Land Management (BLM) has traditionally lacked a standardized methodology for determining if a newly drilled geothermal well is "producible," a designation essential for deciding whether a lease should be "held by production." This is a straightforward problem to solve in oil and gas: Demonstrate that a well is economically viable, meaning it produces sufficient oil or gas to exceed direct operating costs and lease-related expenses, such as rentals or minimum royalties. In geothermal, the problem is more complex: Geothermal wells are tightly coupled with the downstream infrastructure - specifically, the power plant, which is often not designed until well after a lease is deemed as "held by production." Although this designation is critical for advancing geothermal power plant development on BLM-managed lands, current geothermal well assessments often rely on ad hoc approaches that can be complex, operator-biased, and heavy in assumptions related to economic viability. To address this, we have developed two complementary methodologies: a minimum power requirement-based approach and a productivity index (PI)-based approach. These methods leverage key flow test data - pressure, temperature, flow rate, and specific enthalpy - to provide reliable and standardized producible well determinations. The minimum power requirement-based approach evaluates wells against specific power output thresholds informed by reservoir experts and the associated temperature requirements. The PI-based approach assesses well productivity using widely accepted reservoir engineering metrics, proposing a threshold of 2.5 kg/s/bar. Both methods are data-driven and grounded in empirical production data from operational geothermal wells, avoiding uncertain economic assumptions while maintaining decision-making accuracy. Wells falling below key performance thresholds (i.e., PI, specific power) are deemed non-producible. These methodologies aim to streamline BLM's decision-making process, reduce nontechnical barriers to geothermal energy adoption, and enable regulatory expansion into states lacking geothermal expertise. Preliminary results indicate clear trends and thresholds in production data that provide actionable insights for evaluating well producibility. Validation using well completion report (WCR) data is ongoing, with promising results demonstrating the potential for these standardized methodologies to impact geothermal development significantly.

15 GEOTHERMAL ENERGY↗

Utah FORGE: Neubrex Well 16B(78)-32 DAS Data - April, 2024

This dataset comprises Distributed Acoustic Sensing (DAS) data collected from the Utah FORGE monitoring well 16B(78)-32 (the producer well) during hydraulic fracture stimulation operations conducted in April 2024. The data were acquired continuously over the stimulation period at a temporal sampling rate of 10,000 Hz (10 kS/s) and a spatial resolution of approximately 3.35 feet (1.02109 meters). The measurements were captured using a Neubrex NBX-S4100 Time Gated Digital DAS interrogator unit connected to a single-mode fiber optic cable, which was permanently installed within the casing string. All recorded channels correspond to downhole segments of the fiber optic cable, from a measured depth (MD) of 5,369.35 feet to 10,352.11 feet. The DAS data reflect raw acoustic energy generated by physical processes within and surrounding the well during stimulation activities at wells 16A(78)-32 and 16B(78)-32. These data have potential applications in analyzing cross-well strain, far-field strain rates (including microseismic activity), induced seismicity, and seismic imaging. Metadata embedded in the attributes of the HDF5 files include detailed information on the measured depths of the channels, interrogation parameters, and other acquisition details. The dataset also includes a recording of a seminar held on September 19, 2024, where Neubrex's Chief Operating Officer presented insights into the data collection, analysis, and preliminary findings. The raw data files, stored in HDF5 format, are organized chronologically according to the recording intervals from April 9 to April 24, 2024, with each file corresponding to a 12-second recording interval.

15 GEOTHERMAL ENERGY↗

PRIMO - The P&A Project Optimizer

In support of the Methane Emissions Reduction Program (MERP) and under the National Methane Emissions Reduction Initiative (NEMRI), the National Energy Technology Laboratory (NETL) and NETL site support contractors are developing and releasing an open-source decision-support tool (“PRIMO”) to help organizations determine which marginal conventional wells (MCWs) or other low-producing wells make the best candidates for plugging utilizing MERP funds, while also optimizing subsequent plugging and abandonment (P&A) campaigns for both program impact and efficiency. The framework—which is fully customizable—provides three main capabilities: 1. Ranking candidate wells (based on user preferences) 2. Identifying high-impact, high-efficiency P&A project candidates (with transparently computed scores for relevant metrics) 3. Comparing competing P&A projects quantitatively (through transparently computed project impact and efficiency scores) As such, PRIMO is a versatile, fully customizable tool that is meant to support organizations in making data-based, transparent, and defensible well selection and P&A project design decisions. For questions, comments or feedback, please contact primo@netl.doe.gov.

MERP↗

Development of a forced advection sampling technique (FAST) for quantification of methane emissions from orphaned wells

Abstract. Orphaned wells, meaning unplugged and non-producing wells lacking responsible owners, pose a significant and undersampled environmental challenge due to their vast number and unknown associated emissions. We propose, develop and test an alternative method for estimating emissions from orphaned wells using a forced advection sampling technique (FAST) that can overcome many of the limitations in current methods (cost, accuracy, safety). In contrast to existing ambient Gaussian plume methods, our approach uses a fan-generated flow to force advection between the emission source and a point methane (CH4) sensor. The fan flow field is characterized using a colocated sonic anemometer to measure the 3D wind profile generated by the fan. Using time-series measurements of CH4 concentration and wind, a simple estimate of the CH4 emission rate of the source can be inferred. The method was calibrated using outdoor controlled-release experiments and then tested on four orphaned wells in Lufkin, TX, and Osage County, OK. Our results suggest that the FAST method can provide a low-cost, portable, fast and safe alternative to existing methods with reasonable estimates of orphaned well emissions over a range of leak rates below 40 g h−1 and within certain geometric and atmospheric constraints.

Dubey, Mohit L↗

Reservoir-scale model of geologic hydrogen production from serpentinization: Cyclic injection in a dual-permeability fracture-matrix system

Geologic hydrogen (GeoH 2 ) from serpentinization is a promising low-carbon resource, but its reservoir-scale behavior remains poorly understood. We develop a dual-permeability reactive transport model for an injector–producer well pair in ultramafic rock that couples multiphase flow, heat transfer, geochemistry, and porosity–permeability evolution. Here, the model is calibrated to olivine flow-through experiments and upscaled to two-year long simulations with continuous injection and cyclic injection with shut-in-to-injection ratios (SIR = 1, 0.5, 0.1). Olivine reacts along high-flux pathways to form lizardite and magnetite, increasing pH; H 2 (aq) and H 2 (g) peak early and then decline as exsolution and advective export outpace local generation. Continuous injection yields the highest cumulative H 2 but the lowest water-use efficiency (1.138 x 10 –6 mol/kgw). Cyclic injection increases this ratio to 1.35 x 10 –6 , 1.377 x 10 –6 , and 1.182 x 10 –6 mol/kgw for SIR = 1, 0.5, and 0.1, respectively; SIR = 0.5 provides a ~20% improvement over continuous injection and the best compromise for pilot design.

08 HYDROGEN↗

Process Intensification by One-Step, Plasma-Assisted Catalytic Synthesis of Liquid Chemicals from Light Hydrocarbons

In this project, we present a plasma-assisted, catalytic process that can handle variations in production rates and gas compositions of producing wells and reliably converts the hydrocarbons to gas phase olefins, high molecular weight alkanes, and liquid chemicals. The low-temperature plasma serves as a reactive chemical environment that activates and converts the light hydrocarbons to these valuable products. We present results from the integration of a catalyst into the low-temperature plasma zone to improve reaction efficiency and product selectivity. We show that the gas composition, bulk gas temperature, and input power of the plasma, with and without catalysts, influence the production rates of liquids from natural gas feeds.

03 NATURAL GAS↗

Newberry SHR Demonstration Project – Bipartisan Infrastructure Law Enhanced Geothermal (EGS) Pilot Demonstration (Abstract)

This project will create an Engineered Geothermal System (EGS) comprising two or more wells drilled to a depth of 4.25 km into superhot rock (SHR) with a temperature of 425 °C at Newberry Volcano in Central Oregon. An EGS is a manufactured heat exchanger in which water is injected in a deep injection well, or injector, to extract heat from the hot rock at depth and steam is returned to the surface in a production well, or producer, to generate electricity. In this project, the SHR EGS will be made using new methods and technologies to stimulate and connect hydraulic and natural fractures to enable multiple flow pathways between wells, allowing for optimal heat mining from the reservoir rock. The new technologies are designed to operate at rock temperatures much higher than those encountered in traditional geothermal. Following EGS completion, water will be injected into the injector well and steam extracted from the producer well in a long-term connectivity flow test demonstrating SHR reservoir evolution with time and use. Success will be measured by demonstrating the efficacy of new technologies and by producing economic quantities of steam (>40 MWth).

15 GEOTHERMAL ENERGY↗

Intrabasin Comparison of the Microbiology and Geochemistry of Produced Fluid From Hydraulically Fractured Wells in the Permian Region

The Permian Basin is the highest producing oil reservoir in the United States. Hydrocarbon extraction methods in this region are often associated with frac hits, or interwell communication events where an established well is affected by the pumping of fracture fluid into a new well. Our previous work revealed a geochemical signal indicating the presence of frac hits in the Permian Basin. We returned to this area with the goal of expanding our understanding of subsurface interactions common in this region. To do so, we collected produced water from 25 unique sites across the Permian Basin, 10 of which had previously been characterized during an active frac hit. For each sample, we measured the pH, alkalinity, geochemistry, microbial load, and microbial community composition. Permian Basin produced water is characterized by higher sulfate and lower total dissolved solids (TDS) concentrations compared to other regions. Interestingly, wells impacted by frac hits have a geochemical profile that resembles that of fracture fluid, with both lowered sulfate and lowered TDS concentrations compared to unaffected wells. Due to the year-long recovery window between sample collection periods, we anticipate that all our data will be characterized by the typical high sulfate, low TDS concentrations.

geochemistry↗

Intrabasin Comparison of Produced Fluid From Hydraulically Fractured Wells in the Permian Region

The Permian Basin is the highest producing oil and gas reservoir in the United States. Hydrocarbon extraction methods in this region are often associated with frac hits, or interwell communication events where an established well is affected by the pumping of fracture fluid into a new well. Our previous work revealed a unique geochemical signal indicating the presence of frac hits in the Permian Basin. We returned to this area with the overall goal of expanding our understanding of the microbial and geochemical dynamics common in this region. To do so, we collected produced water from 25 unique sites across the Permian Basin, 10 of which had previously been characterized during an active frac hit with the rest being novel. For each sample, we measured the pH, alkalinity, geochemical composition, microbial load (qPCR), and microbial community composition (16S rRNA sequencing). Permian Basin produced water is characterized by higher sulfate and lower total dissolved solids (TDS) concentrations compared to other regions. Interestingly, wells impacted by frac hits have a geochemical profile that resembles that of fracture fluid, with both lowered sulfate and lowered TDS concentrations compared to unaffected wells in this region. Due to the year-long recovery window between sample collection periods, we anticipate that all of our data will be characterized by the typical high sulfate, low TDS concentrations.

environmental microbiology↗

Magnon orbital angular momentum of ferromagnetic honeycomb and zigzag lattice models

By expanding the gauge 𝜆 𝑛 ⁡(𝐤) for magnon band 𝑛 in harmonics of momentum 𝐤=(𝑘,𝜙), we demonstrate that the only observable component of the magnon orbital angular momentum 𝑂 𝑛 ⁡(𝐤) is its angular average over all angles 𝜙, denoted by 𝐹 𝑛 ⁡(𝑘). Although 𝐹 𝑛 ⁡(𝑘) vanishes for antiferromagnetic honeycomb and zigzag (0<𝐽 1 <𝐽 2 ) lattices, it is nonzero for the ferromagnetic (FM) versions of those lattices in the presence of Dzyaloshinskii-Moriya interactions. For a FM zigzag model with equal exchange interactions 𝐽 1⁢𝑥 and 𝐽 1⁢𝑦 along the 𝑥 and 𝑦 axes, the magnon bands are degenerate along the boundaries of the Brillouin zone with 𝑘 𝑥 −𝑘 𝑦 =±𝜋/𝑎 and the Chern numbers 𝐶 𝑛 are not well defined. However, a revised model with 𝐽 1⁢𝑦 ≠𝐽 1⁢𝑥 lifts those degeneracies and produces well-defined Chern numbers of 𝐶𝑛=±1 for the two magnon bands. When 𝐽 1⁢𝑦 =𝐽 1⁢𝑥 , the thermal conductivity 𝜅 𝑥⁢𝑦⁡ (𝑇) of the FM zigzag lattice is largest for 𝐽2/𝐽1>6 but is still about four times smaller than that of the FM honeycomb lattice at high temperatures. Due to the removal of band degeneracies, 𝜅𝑥⁢𝑦⁡(𝑇) is slightly enhanced when 𝐽 1⁢𝑦 ≠𝐽 1⁢𝑥 .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Utah FORGE: Well 16A(78)-32 and 16B(78)-32 Stimulation Production Logging Tests

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.

15 GEOTHERMAL ENERGY↗

Bayesian batch optimization for molybdenum versus tungsten inertial confinement fusion double shell target design

Access to reliable, clean energy sources is a major concern for national security. Much research is focused on the “grand challenge” of producing energy via controlled fusion reactions in a laboratory setting. For fusion experiments, specifically inertial confinement fusion (ICF), to produce sufficient energy, the fusion reactions in the ICF fuel need to become self-sustaining and burn deuterium-tritium (DT) fuel efficiently. The recent record-breaking NIF ignition shot was able to achieve this goal as well as produce more energy than used to drive the experiment. This achievement brings self-sustaining fusion-based power systems closer than ever before, capable of providing humans with access to secure, renewable energy. In order to further progress toward the actualization of such power systems, more ICF experiments need to be conducted at large laser facilities such as the United States's National Ignition Facility (NIF) or France's Laser Mega-Joule. The high cost per shot and limited number of shots that are possible per year make it prohibitive to perform large numbers of experiments. As such, experimental design relies heavily on complex predictive physics simulations for high-fidelity “preshot” analysis. These multidimensional, multi-physics, high-fidelity simulations have to account for a variety of input parameters as well as modeling the extreme conditions (pressures and densities) present at ignition. Such simulations (especially in 3D) can become computationally prohibitive to turn around for each ICF experiment. In this work, we explore using Bayesian optimization with Gaussian processes (GPs) to find optimal designs for ICF double shell targets, while keeping computational costs to manageable levels. These double shell targets have an inner shell that grades from beryllium on the outer surface to the higher Z material molybdenum, as opposed to the nominally used tungsten, on the inside in order to trade off between the high performance associated with high density inner shells and capsule stability. We describe our results for “capsule-only” xRAGE simulations to study the physics between different capsule designs, inner shell materials, and potential for future experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Antimicrobial activity of peptoids against Metallo-β-lactamase-producing Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa , and other WHO priority pathogens, including Candida auris

Abstract Aims The World Health Organization has identified ESKAPE bacteria and Candida auris as priority pathogens, emphasizing an urgent need for novel antimicrobials to combat them. This study aimed to explore the therapeutic potential of antimicrobial peptidomimetics, specifically peptoids with sequence-specific N-substituted glycines, against ESKAPEE pathogens, including metallo-β-lactamase (MBL) producers, as well as C. auris strains. Methods and results This study evaluated activity of the peptoids against the multidrug-resistant priority pathogens. The peptoid TM8 (with an N-decyl alkyl chain) demonstrated a geometric mean minimum inhibitory concentration (MIC) of 7.8 μg ml−1 against MBL-producing bacteria, and 5.5 μg ml−1 against C. auris. TM8 showed synergy with ciprofloxacin, enhancing its effectiveness 4-fold against NDM-1-producing Klebsiella pneumoniae. No antagonism was seen when TM8 was used with either conventional antibiotics or antifungals. Peptoids that had therapeutic indices below 3 were generally more hydrophobic, due to either alkyl chains or bromine. Scanning electron microscopy and live-dead staining assay on peptoid-treated C. auris confirmed morphological changes and killing activity, respectively. Furthermore, the peptoid could effectively inhibit biofilm formation by C. auris. Conclusion Peptoids demonstrated antibacterial activity against ESKAPEE, particularly against MBL-producing Gram-negative bacteria. Additionally, they exhibited antifungal and anti-biofilm activities against C. auris strains.

Biotechnology & Applied Microbiology↗

Template‐Directed Growth of Palladium Nanoparticles in Holey Single‐Layer Graphene for High‐Performance Room‐Temperature Hydrogen Sensing

Holey graphene (HG), formed by introducing nanoscale perforations into graphene sheets, combines the structural advantages of continuous sp 2 conjugation with the beneficial effects of high surface area and enhanced chemical reactivity associated with nanoscopic holes. Conventional HG fabrication methods often rely on harsh oxidative treatments that compromise graphene's intrinsic electronic properties by disrupting its sp 2 conjugation. A new approach is introduced to fabricate HG directly from single-layer graphene (SLG), using two-dimensional covalent organic frameworks (COFs) as a template followed by controlled oxygen plasma etching. This method preserves the integrity of the SLG's sp 2 network while producing well-defined holes with an average diameter of 2.7 nm. These hole edges act as reactive sites that facilitate the confined, autoreductive growth of palladium nanoparticles (PdNPs) without external reducing agents. The confined hole geometry prevents NP agglomeration and ensures a uniform size distribution. The resulting Pd@HG hybrid exhibits exceptional chemiresistive hydrogen sensing characteristics, including ultrahigh sensitivity, low detection limits, rapid response and recovery, and long-term stability under both dry and humid conditions. Mechanistic investigations reveal a two-step sensing process involving surface redox interactions and hydrogen absorption into the PdNP lattices. This strategy presents a scalable platform for integrating metal NPs within conductive carbon frameworks for advanced sensing applications.

chemical vapor deposition↗

High-resolution modeling of indoor radon exposure with uncertainty quantification in Utah

Indoor radon accounts for 37% of population-level exposure to ionizing radiation in the United States. However, radon metrics are typically reported at coarse spatial scales, potentially obscuring meaningful local variation. We developed a high-resolution modeling framework to estimate indoor radon concentrations across Utah while explicitly quantifying predictive uncertainty. A total of 19,497 residential radon measurements collected between 2006 and 2017 were combined with environmental and housing characteristics and analyzed using a geospatial neural network that accommodates spatial dependence and nonlinear associations. Predictions were generated on a uniform hexagonal grid at 0.73 km2 resolution (H3 level 8). Out-of-sample predictions aggregated to the H3 level 8 grid showed good agreement with observed concentrations (Pearson r=0.64), while household-level predictions exhibited more moderate agreement (r=0.45). The model produced well-calibrated uncertainty estimates, with 24.1% of held-out observations exceeding the predicted 75th-percentile threshold. Maps of predicted radon concentrations and the probability of exceeding the U.S. EPA action level of 148 Bq/m3 (4 pCi/L) revealed substantial fine-scale spatial heterogeneity that was not apparent in conventional coarse-resolution summaries, with greater local variability observed in densely monitored urban counties than in sparsely sampled regions. High-resolution radon models that explicitly quantify uncertainty provide a useful framework for characterizing the spatial distribution of indoor radon and identifying areas of elevated exceedance risk. These findings highlight the value of fine-scale monitoring data and uncertainty-aware modeling approaches for radon exposure assessment, environmental risk characterization, and radon-related health research.

Wu, Yunhan [ORNL] (ORCID:0000000178842994)↗

Bayesian Optimized Deep Ensemble for Uncertainty Quantification of Deep Neural Networks: a System Safety Case Study on Sodium Fast Reactor Thermal Stratification Modeling

Deep neural networks (DNNs) are increasingly important to scientific computing and engineering system simulations. Accurate uncertainty quantification (UQ) for DNNs is critical in safety-sensitive engineering domains. Traditional Deep Ensemble (DE) methods, while easy to implement, frequently suffer from poorly calibrated uncertainty estimates and limited predictive accuracy due to reliance on fixed architectures with varied weight initializations. To address these issues, we introduce a workflow that combines Bayesian Optimization (BO) and DE. The workflow is modular, scalable, and integrates parallel BO initialized with Sobol sequences to individually optimize the hyperparameters of each ensemble member. This method enhances ensemble diversity, improves predictive accuracy, and provides reliable uncertainty estimates. We evaluate the proposed BODE approach in a sodium fast reactor thermal stratification modeling case study, where we used a densely connected convolutional neural network to predict turbulent viscosity during the reactor transient with consideration of data noise. We benchmark its performance against several optimization approaches, including baseline deep ensemble, evolutionary algorithm-optimized ensemble, ensemble formed via random search combined with greedy selection, and a BO ensemble using random initialization. Here, our results demonstrate superior performance of the developed BODE approach. In noise-free scenarios, BODE notably reduces incorrect aleatoric uncertainty and significantly enhances predictive accuracy. Under conditions of 5% and 10% Gaussian noise, BODE adaptively quantifies uncertainty proportional to data noise, achieving up to an 80% reduction in root mean square error compared to baseline methods and producing well-calibrated prediction intervals.

Bayesian optimization↗