Engineering topics

Kamdar, Harshil

Publications and source records attributed to Kamdar, Harshil.

5 records

DOE OSTI2026

Seasonality and Declining Intensity of Methane Emissions from the Permian and Nearby US Oil and Gas Basins

We quantify weekly methane emissions and trends from oil and gas production in the US Permian Basin for 2019-2023, and in nearby basins for 2022-2023, by analytical inversion of Tropospheric Monitoring Instrument (TROPOMI) satellite observations with the Integrated Methane Inversion (IMI) at 25 km resolution. Permian oil and gas emissions averaged 4.0 ± 1.1 Tg a-1 over 2019-2023, with large seasonal variation but little interannual variability. Methane intensity fell from 5.2 to 3.2% as production surged. Intensity in the New Mexico Permian fell from 4.5 to 2.1%, approaching the state's 2026 target of <2%. Emissions were on average 50 ± 10% higher in winter than summer, which we corroborate with Permian Basin Tower Network measurements, Insight M aircraft data, and GHGSat satellite observations. This seasonality may be driven in part by higher winter emissions from liquid storage tanks due to decreased separator efficiency in cold conditions. Similar but weaker seasonality along with decreasing emissions and intensities is found in weekly inversions for the Anadarko, Barnett, Eagle Ford, and Haynesville basins in 2022-2023. Our work suggests that better weatherization of oil and gas facilities could significantly reduce methane emissions.

Permian↗

DOE OSTI2025

Creating Accurate Methane Emission Inventories through Data-Driven Airborne Survey Strategies

Because natural gas emits less carbon than other fossil fuels, it holds promise as a green energy transition fuel. However, the overall carbon footprint of natural gas is significantly elevated by methane emissions that occur during its production and transmission (Cusworth et al. 2022). Methane â€œsuper-emitters,â€ while comprising only about 1% of sites, are responsible for the majority of oil- and gas-sourced methane emissions, making their detection and mitigation critical in reducing the climate impact of natural gas and in meeting national and global sustainability goals (Sherwin et al. 2024). Yet, despite advancements in detection, significant uncertainties remain regarding the size, frequency, and duration distributions of methane emissions (e.g., Frankenberg et al. 2016, Cusworth et al. 2022, Chen, Sherwin et al. 2022, Conrad et al. 2023, Johnson et al. 2023, Sherwin et al. 2024) underscoring the need for comprehensive emissions inventories segmented by basin across the US. Airborne surveys are well-suited for collecting data to build these comprehensive, basin-level inventories because they allow for extensive spatial coverage, and have the spatial resolution, and the sensitivity to pinpoint individual methane sources. As remote sensing technologies enable rapid basin-scale surveys, it is imperative to establish scientifically and statistically robust standards to generate reliable and actionable emissions inventories. Recent work has shown that differences in airborne sampling strategies, detection technologies, and analysis can lead to large differences between survey conclusions if not correctly accounted for (Chen et al. 2024). This elevates the importance of incorporating proper sampling and analysis techniques when designing a methane emissions monitoring campaign to produce accurate results and facilitate cross-study comparisons. In this paper, we describe a survey strategy designed using the latest conclusions from the literature to align results from different aerial surveys. We identify several sampling and analysis principles, including large sample sizes, balanced sampling across oil and gas production, careful survey area definition, and a unified protocol for analysis, to be vital to producing an unbiased estimate of basin-scale emissions. We present results from a Department of Energy-funded project that deployed this survey strategy in two understudied oil and gas- producing regions in the United States: the Haynesville Basin in Texas and Louisiana, and the Woodford Shale in the Anadarko Basin in Oklahoma.

03 NATURAL GAS↗

DOE OSTI2024

Creating Accurate Methane Emission Inventories through Data-Driven Airborne Survey Strategies: Methods and Results from the Haynesville, Anadarko, and Permian Basins

Significantly reducing methane emissions from the oil and gas sector can decrease the rate of climate change over the next two decades, buying critical time for a global energy transition. However, emissions inventories that can be used by oil and gas operators and environmental regulators to identify optimal methane emission mitigation strategies are either based on conservative emission factor methods, or are inconsistent between studies due to differences in sampling strategies or survey technologies. We developed a new approach for methane emissions survey design that yields representative basinwide methane emissions inventories by surveying a subset of total assets in a given oil and gas basin. We identify several sampling and analysis principles, including large sample sizes, balanced sampling across oil and gas production, careful survey area definition, and a unified protocol for analysis, to be vital to producing an unbiased estimate of basin-scale emissions that can be reconciled with future studies. We further present results from deploying this strategy in two oil and gas producing regions in the United States: the Haynesville Basin in Texas and Louisiana, and the Woodford Shale in the Anadarko Basin in Oklahoma. Aerial surveys were performed in 2023 using the Insight M LeakSurveyor™ technology. Preliminary results from methane emissions detected by Insight M indicate that aerially detected emissions above roughly 30 kg(CH4)/hr by themselves contribute a fractional loss rate of 1.13% of gross gas production across oil and gas operations in the Haynesville Basin, with aerially detected emissions equivalent to 2.67% of gross gas production in the Woodford Shale. We supplement these aerial estimates with modeled emissions that are below the LeakSurveyor’s survey sensitivity using a recently published inventory-based model of methane emissions, which we update for our survey areas. We then combine our aerial detections with modeled emissions to yield methane emission distributions and inventories that incorporate the full range of potential methane emissions from the smallest to the largest. These results can be used to identify the most effective methane mitigation strategies for our study areas, and can be reconciled with future methane emissions surveys that use different technologies.

Sherwin, Evan (ORCID:0000000321804297)↗

DOE OSTI2021

Spatial and Kinematic Clustering of Stars in the Galactic Disk

The Galactic disk is expected to be spatially and kinematically clustered on many scales due to both star formation and the Galactic potential. In this work we calculate the spatial and kinematic two-point correlation functions (TPCF) using a sample of 1.7 × 10 6 stars with radial velocities from Gaia DR2. Clustering is detected on spatial scales of 1–300 pc and a velocity scale of 15 km s –1 . After removing bound structures, the data have a power-law index of γ ≈ –1 for 1 pc < Δr < 100 pc and γ ≲ –1.5 for Δr > 100 pc. We interpret these results with the aid of a star-by-star simulation of the Galaxy, in which stars are born in clusters orbiting in a realistic potential that includes spiral arms, a bar, and giant molecular clouds. We find that the simulation largely agrees with the observations at most spatial and kinematic scales. In detail, the TPCF in the simulation is shallower than the data at ≲20 pc scales, and steeper than the data at ≳30 pc. We also find a persistent clustering signal in the kinematic TPCF for the data at large Δv (>5 km s –1 ) that is not present in the simulations. We speculate that this mismatch between observations and simulations may be due to two processes: hierarchical star formation and transient spiral arms. Furthermore, we also predict that the addition of ages and metallicities measured with a precision of 50% and 0.05 dex, respectively, will enhance the clustering signal beyond current measurements.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Distant Relatives: The Chemical Homogeneity of Comoving Pairs Identified in Gaia

Comoving pairs, even at the separations of $\mathcal{O}$(10 6 ) are a predicted reservoir of conatal stars. Here we present detailed chemical abundances of 62 stars in 31 comoving pairs with separations of 10 2 –10 7 au and 3D velocity differences <2 km s -1 . This sample includes both bound comoving pairs/wide binaries and unbound comoving pairs. Observations were taken using the Magellan Inamori Kyocera Echelle (MIKE) spectrograph on board the Magellan/Clay Telescope at high resolution (R~ 45,000) with a typical signal-to-noise ratio of 150 pixel -1 . With these spectra, we measure surface abundances for 24 elements, including Li, C, Na, Mg, Al, Si, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Sr, Y, Zr, Ba, La, Nd, and Eu. Taking iron as the representative element, our sample of wide binaries is chemically homogeneous at the level of 0.05 dex, which agrees with prior studies on wide binaries. Importantly, even systems at separations 2 × 10 5 –10 7 au are homogeneous to 0.09 dex, as opposed to the random pairs, which have a dispersion of 0.23 dex. Assuming a mixture model of the wide binaries and random pairs, we find that 73 ± 22% of the comoving pairs at separations 2 × 10 5 –10 7 au are conatal. Our results imply that a much larger parameter space of phase space may be used to find conatal stars, to study M-dwarfs, star cluster evolution, exoplanets, chemical tagging, and beyond.

79 ASTRONOMY AND ASTROPHYSICS↗