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DOE OSTI2025

Integrated System for Methane Emissions Monitoring, Mapping, and Quantification

This report presents the work completed under the DOE iM4 project for the development of a methane emission monitoring system for detection, location, and quantification of methane in oil and gas industries. The task was divided into four main areas including: 1) Sensors and Input, 2) Centralized Cloud Information Center, 3) Algorithms, and 4) Testing and Validation. Task 1 focused on researching and developing an understanding of the current, or soon to be, available methane sensing technologies. Task 2 consisted of developing the architecture, selecting hardware, software and elements for the methane monitoring system. Task 3 focused on the algorithms used for the complex inverse model of going from measured methane signatures to the detection, localization, and quantification of sources that are desired. Finally, Task 4 focused on the methods of testing and validating the operation of the system. Attention was also given to the development method and cost breakdown of the system.

03 NATURAL GAS↗
DOE OSTI2022

Monitoring Methane Emissions from Oil and Gas Operations

The atmospheric concentration of methane has more than doubled since the start of the Industrial Revolution. Methane is the second-most-abundant greenhouse gas created by human activities and a major driver of climate change. This APS-Optica report provides a technical assessment of the current state of monitoring U.S. methane emissions from oil and gas operations, which accounts for roughly 30% of U.S. anthropogenic methane emissions. The report identifies current technological and policy gaps and makes recommendations for the federal government in three key areas: methane emissions detection, reliable and systematized data and models to support mitigation measures, and effective regulation.

02 PETROLEUM↗
DOE OSTI2020

Sensor Placement Optimization Software Applied to Site-Scale Methane-Emissions Monitoring

Advances in sensor technology have increased our ability to monitor a wide range of environments. However, even as the cost of sensors decline, only a limited number of sensors can be installed at any given site. The physical placement of sensors, along with the sensor technology and operating conditions, can have a large impact on our ability to adequately monitor environmental change. This paper introduces a new open-source Python package, called Chama, that determines optimal sensor placement and technology to improve a sensor network’s detection capabilities. Additionally, the methods are demonstrated using site-specific methane emission scenarios that capture uncertainty in wind conditions and emission characteristics. Mixed-integer linear programming formulations are used to determine sensor locations and detection thresholds that maximize detection of the emission scenarios. The optimized sensor networks consistently increase the ability to detect leaks, as compared to sensors placed near each potential emission source or along the perimeter of the site.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Integrated Methane Monitoring Platform Extension, Volume I: Final Technical Report

The IMMPE project, DE-FE0032284, was to enhance methane monitoring technologies and their applications across various natural gas asset classes. The scope included deploying advanced methane detection and monitoring technologies to identify and mitigate fugitive methane emissions, measuring emission rates, and assessing impacts. The findings included the successful mitigation of identified emissions and quantification of emission rates. A key outcome was the development of a comprehensive template and summary of recommendations for methane emissions monitoring, which is replicable for both upstream and downstream applications. Furthermore, the project emphasized the importance of education by providing training opportunities for technicians and regulators, thereby fostering awareness and promoting the adoption of cost-effective methane emissions monitoring and management techniques.

02 PETROLEUM↗
DOE OSTI2024

Multi-Scale Integrated Monitoring System for Enhancing Methane Emission Detection, Quantification & Prediction

This report details the progress and findings of a comprehensive study on reviewing existing solutions, identifying technology gaps, and formulating an “all-in-one” integrated strategy for developing the next-generation multiscale methane monitoring and modeling platform, conducted under grant number DE-FE0032292. Co-led by Dr. David Ebert, Dr. Binbin Weng, and Dr. Chenghao Wang at the University of Oklahoma, the project’s goal was to develop an integrated approach for building this engineering platform to detect, quantify, and mitigate methane emissions across various temporal scale, spatial scales, and sectors. The planning grant study began with an extensive review of various methane sensing and monitoring technologies and systems, surveying over 100 technology providers globally. This review revealed the prevalence of optical methods over chemical methods in commercially available sensors, with Non-Dispersive Infrared (NDIR), Tunable Diode Laser Absorption Spectroscopy (TDLAS), and Optical Gas Imaging (OGI) cameras being the most prevalent options. A trend towards more advanced optical techniques was observed, driven by increased regulatory focus and technological advancements. The technical evaluation of these sensing technologies provided crucial insights into their capabilities and limitations. The study examined emerging technologies such as Differential Absorption LiDAR (DIAL), which show promise for high-precision and long-range detection. The team then investigated the features and application bandwidth of various sensing platforms, including handheld, fixed/stationary, mobile, aerials, and spaceborne monitors. Pilot field studies were conducted to assess the capabilities of solutions for different emission scenarios. Field work with sensor deployments was conducted at three distinct site types: an oil & gas industry site, a cattle ranching operation, and a waste processing facility. The team also conducted a thorough review of methane flux inverse modeling approaches, focused on physically based methods. These approaches were categorized into simple, intermediate, and advanced methods. A realtime WRF-GHG (Weather Research and Forecasting-Greenhouse Gas) modeling system was developed and applied, incorporating multiple data sources to guide field experiments and inform methane plume detection. The project identified and analyzed numerous categories of methane data sources, including satellite measurements, ground-based sensors, and inventory databases. Key platforms examined include EDGAR, EPA GHGI, NASA TROPOMI, Carbon Mapper, and Climate TRACE, among others. The team proposed an architecture for a comprehensive methane monitoring platform. This system incorporates multi-source data acquisition, advanced data processing and assimilation, interactive visualization tools, and analytical capabilities for emissions forecasting and scenario analysis. The proposed platform aims to provide a user-friendly interface catering to various stakeholders, from researchers to policymakers. The architecture includes sophisticated data ingestion methods, a centralized data warehouse, and advanced analytical tools for data fusion and interpretation. To ensure the relevance and effectiveness of the proposed system, a comprehensive survey was conducted to gather stakeholder input on system requirements. Key findings include a strong need for integrating various data types and formats, a preference for real-time data updates and advanced visualization tools, and a demand for user-friendly interfaces catering to different expertise levels.

03 NATURAL GAS↗
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 “super-emitters,” 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 OSTI2025

Advancing Development of Emissions Detection (Final Report)

This document is the final report to the U.S. Department of Energy (DOE for contract DE-FE0031873) awarded to Colorado State University (CSU). CSU and partners at Harrisburg University of Science and Technology, University of Texas Arlington, and University of Texas at Austin organized several testing rounds to provide knowledge focused on advancing the detection capabilities of emissions monitoring devices. With this funding opportunity the research group began by establishing the Advancing the Development of Emission Detection (ADED) program, with the goal focused on enhancing the accuracy, reliability, and field applicability of methane detection technologies. This effort aimed to address the critical challenges of identifying and quantifying methane emissions while enabling industry stakeholders to meet regulatory compliance and environmental sustainability goals. The program engaged with industry, government, and technology stakeholders to promote adoption and consensus on testing techniques for methane detection solutions. Methane, a potent greenhouse gas, contributes significantly to global warming, and the oil and natural gas (O&G) sector is a primary source of methane emissions. Regulatory measures such as leak detection and repair (LDAR) programs have been implemented to address emissions. However, traditional LDAR approaches, reliant on handheld and component-level measurements, are resource-intensive. To address these limitations and move with evolving regulations, advanced methane technologies are emerging. These solutions include ground-based sensors, mobile systems (e.g., drones, vehicles, and aircraft), and satellite-based platforms. They offer innovative capabilities for autonomous monitoring, larger spatial coverage, and emission quantification using methods such as tracer gas techniques and inverse modeling with Gaussian plume analysis. The ADED program began with creating protocols for methane controlled release (CR) testing these continuous monitoring (CM) and survey technologies that detect and monitor methane emissions at O&G facilities. The protocols were then implemented throughout testing of CM and survey devices at CSU’s Methane Emissions Technology Evaluation Center (METEC) facility from 2021 through 2024. As apart of the protocol, solutions that tested under the ADED program installed their solutions at METEC, documented their system under test, and provided detection reports to the METEC team for analysis. The METEC team would provide the solutions with analyzed reports of their emissions and ground truth data of the releases conducted during their testing session. Under the ADED program, CMs were also tested at O&G facilities for a six week test run of challenge release (ChR) releases. The findings from the ADED program underscore the critical role of collaborative research and innovation in tackling methane emissions, offering a pathway for the oil and gas sector to achieve significant environmental and economic benefits. Results from METEC testing saw improvement of performance and accuracy across all solutions over the extent of the ADED experiments. The results also showed a variance in CM solution performance between CRs and ChRs. That variance pushed the team to further analyze the differences between CR testing environments and field conditions. With the drive from regulations and that variance in field conditions, the ADED team began designing a new CR testing protocol and additions to the METEC testing facility. The METEC team is furthering the progress made through the ADED program with awarded funding from DE-FE0032276. This funding pushes the development of METEC’s addition with new equipment, allowing for an updated facility layout. METEC still facilitates for traditional facilities, with a legacy pad, while expanding an new design based on how O&G infrastructure has Final Report - Contract Number: DE-FE0031873 been changed over the last decade. Throughout the ADED program the team has also been working with international partners to ensure staying in the trend globally. International partners have been essential in moving the new protocol forward to implement into CR testing at the METEC facility in Spring 2025.

42 ENGINEERING↗
DOE OSTI2024

Defining Detection Limits for Continuous Monitoring Systems for Methane Emissions at Oil and Gas Facilities

Networks of fixed-point continuous monitoring systems are becoming widely used in the detection and quantification of methane emissions from oil and gas facilities in the United States. Regulatory agencies and operators are developing performance metrics for these systems, such as minimum detection limits. Performance characteristics, such as minimum detection limits, would ideally be expressed in emission rate units; however, performance parameters such as detection limits for a continuous monitoring system (CMS) will depend on meteorological conditions, the characteristics of emissions at the site where the CMS is deployed, the positioning of CMS devices in relation to the emission sources, and the amount of time allowed for the CMS to detect an emission source. This means that certifying the performance of a CMS will require test protocols with well-defined emission rates and durations; initial protocols are now being used in field tests. Field testing results will vary, however, depending on meteorological conditions and the time allowed for detection. This work demonstrates methods for evaluating CMS performance characteristics using dispersion modeling and defines an approach for normalizing test results to standard meteorological conditions using dispersion modeling.

Chen, Qining (ORCID:0000000316908091)↗
DOE OSTI2021

Combined Mixed Potential Electrochemical Sensors and Artificial Neural Networks for the Quantificationand Identification of Methane in Natural Gas Emissions Monitoring

Sensors capable of quantifying methane concentration and discriminating between possible sources are needed for natural gas leak detection where multiple spatially overlapping sources including wetlands and agriculture may be present. We report on the fabrication by an additive manufacturing process of a four electrode La 0.87 Sr 0.13 CrO 3 , Indium Tin Oxide (In 2 O 3 90 wt%, SnO 2 10 wt%), Au, Pt mixed potential electrochemical sensor using yttria-stabilized zirconia (YSZ) as a solid electrolyte to natural gas detection. Artificial neural networks (ANNs) are used to automatically decode the possible source and concentration of methane. The ANNs trained on sensor data are capable of correctly discriminating between three sources of methane emissions from simulated mixtures of emissions from cattle, wetlands, or natural gas with >98% accuracy. Quantification error for methane in mixtures of CH 4 in air, CH 4 + NH3 in air, and simulated natural gas is less than 1.5% ppm when a two-temperature dataset is employed.

03 NATURAL GAS↗
DOE OSTI2020

Historic and modern approaches for discovery of abandoned wells for methane emissions mitigation in Oil Creek State Park, Pennsylvania

Hundreds of oil wells were drilled along Oil Creek in Pennsylvania in the mid-16 1800s, birthing the modern oil industry. No longer in operation, many wells are now classified as abandoned, and, due to their age, their locations are either unknown or inaccurately recorded. These historic well sites present environmental, safety, and economic concerns in the form of possible methane leaks and physical hazards. Airborne magnetic and LiDAR surveys were conducted in the Pioneer Run watershed in Oil Creek State Park to find abandoned wells in a historically significant but physically challenging location. Wells were drilled in this area prior to modern geolocation and legal documentation. Although a large number of old wells were abandoned summarily without remediation of the site, much of the land area within Oil Creek State Park is now covered in trees and dense underbrush, which can obscure wellheads. The thick vegetation and steep terrain limited the possibility of ground-based surveys to easily find well sites for methane emissions studies. The data from remote sensing surveys were used to corroborate potential well locations from historic maps and photographs. Potential well sites were verified in a ground-based field survey and monitored for methane emissions. Two historic photographs documenting oil activity in the late 1800s were georeferenced using a combination of magnetic and LiDAR data. LiDAR data, which were more useful in georeferencing and in field verification, identified 290 field locations in the Pioneer Run watershed, 86% of which were possible well sites. Sixty-two percent of the ground-verified wells remained unplugged and comprised the majority of leaking wells. The mean methane emissions factor for unplugged wells was 0.027 ± 0.099 kg/day, lower than other Appalachian Basin methane emissions estimates. LiDAR was used for the first time, in combination with an airborne magnetic survey, to reveal underground oil industry features and inform well identification and remediation efforts in difficult-to-navigate regions. In the oldest oil fields, where well casing has been removed or wood conductor casing was installed, historic photographs provide additional lines of evidence for oil wells where ground disturbances have concealed surface features. Identification of well sites is necessary for mitigation efforts, as unplugged wells emit methane, a potent greenhouse gas.

02 PETROLEUM↗
DOE OSTI2023

Quantification of oil and gas methane emissions in the Delaware and Marcellus basins using a network of continuous tower-based measurements

Abstract. According to the United States Environmental Protection Agency (US EPA), emissions from oil and gas infrastructure contribute 30 % of all anthropogenic methane (CH4) emissions in the US. Studies in the last decade have shown emissions from this sector to be substantially larger than bottom-up assessments, including the EPA inventory, highlighting both the increased importance of methane emissions from the oil and gas sector in terms of their overall climatological impact and the need for independent monitoring of these emissions. In this study we present continuous monitoring of regional methane emissions from two oil and gas basins using tower-based observing networks. Continuous methane measurements were taken at four tower sites in the northeastern Marcellus basin from May 2015 through December 2016 and five tower sites in the Delaware basin in the western Permian from March 2020 through April 2022. These measurements, an atmospheric transport model, and prior emission fields are combined using an atmospheric inversion to estimate monthly methane emissions in the two regions. This study finds the mean overall emission rate from the Delaware basin during the measurement period to be 146–210 Mg CH4 h−1 (energy-normalized loss rate of 1.1 %–1.5 %, gas-normalized rate of 2.5 %–3.5 %). Strong temporal variability in the emissions was present, with the lowest emission rates occurring during the onset of the COVID-19 pandemic. Additionally, a synthetic model–data experiment performed using the Delaware tower network shows that the presence of intermittent sources is not a significant source of uncertainty in monthly quantification of the mean emission rate. In the Marcellus, this study finds the overall mean emission rate to be 19–28 Mg CH4 h−1 (gas-normalized loss rate of 0.30 %–0.45 %), with relative consistency in the emission rate over time. These totals align with aircraft top-down estimates from the same time periods. In both basins, the tower network was able to constrain monthly flux estimates within ±20 % uncertainty in the Delaware and ±24 % uncertainty in the Marcellus. The results from this study demonstrate the ability to monitor emissions continuously and detect changes in the emissions field, even in a basin with relatively low emissions and complex background conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Supporting Information for the Integrated Methane Monitoring Platform Design (Final Report)

As the urgency for understanding methane emissions and the number of methane monitoring technologies being deployed have increased in the last two decades, there is an opportunity and a need to integrate the numerous disparate data sources to enable the detection, quantification, contextualization, and reporting of methane emissions along the oil and gas supply chain. Such an integration would enable emissions reductions through early detection of super emitters, data-driven mitigation strategies, and improvedgreenhouse gas inventories. The GTI Energy (“GTI”)project team (“the team”) worked with a multitude of industry experts, stakeholders, and subject matter experts (SMEs) to collect guidance, insights, and information to inform the requirements and subsequent engineering, design, deployment, and operations of an integrated methane monitoring platform (IMMP). This final report describes the results of the team’s effort to execute the Integrated Methane Monitoring Platform Design project, ultimately providing an engineering, design, deployment, and operating plan (EDDOP) for the IMMP. This document provides additional information supporting the findings in the final report.

03 NATURAL GAS↗
DOE OSTI2024

Integrated Methane Monitoring Platform Design (Final Report)

As the urgency for understanding methane emissions and the number of methane monitoring technologies being deployed have increased in the last two decades, there is an opportunity and a need to integrate the numerous disparate data sources to enable the detection, quantification, contextualization, and reporting of methane emissions along the oil and gas supply chain. Such an integration would enable emissions reductions through early detection of super emitters, data-driven mitigation strategies, and improved greenhouse gas inventories. The GTI Energy (“GTI”) project team (“the team”) worked with a multitude of industry experts, stakeholders, and subject matter experts (SMEs) to collect guidance, insights, and information to inform the requirements and subsequent engineering, design, deployment, and operations of an integrated methane monitoring platform (IMMP). This final report describes the results of the team’s effort to execute the Integrated Methane Monitoring Platform Design project, ultimately providing an engineering, design, deployment, and operating plan (EDDOP) for the IMMP. This final report summarizes and integrates the project tasks' results and outputs.

03 NATURAL GAS↗
DOE OSTI2021

Frequency Comb-Based Remote Methane Observation Network (Final Scientific/Technical Report)

ARPA-E investment in long-range, frequency comb-based methane detection has resulted in the first scalable, cost-effective continuous methane emissions system. At the onset of this ARPA-E MONITOR award, the dual-frequency comb spectrometer was a sprawling, expensive, laboratory-confined device which had only recently been demonstrated capable of measuring atmospheric trace gases over open-paths. Bringing the technology to the point of being able to monitor for and characterize methane emissions at oil and natural gas production facilities seemed an almost impossible task. Over the subsequent months and years, our team transitioned the Nobel-prize-winning frequency-comb-laser spectrometer technology to a field-deployed regional methane leak detection system. The spectrometer enables highly sensitive near-infrared absorption measurements of methane along kilometer-scale laser beam paths. The measurements are coupled with an atmospheric modeling and inversion framework to triangulate the location of emission sources and quantify the emission rates.

03 NATURAL GAS↗
DOE OSTI2020

Daily Satellite Observations of Methane from Oil and Gas Production Regions in the United States

Production of oil and natural gas in North America is at an all-time high due to the development and use of horizontal drilling and hydraulic fracturing. Methane emissions associated with this industrial activity are a concern because of the contribution to climate radiative forcing. We present new measurements from the space-based TROPOspheric Monitoring Instrument (TROPOMI) launched in 2017 that show methane enhancements over production regions in the United States. In the Uintah Basin in Utah, TROPOMI methane columns correlated with in-situ measurements, and the highest columns were observed over the deepest parts of the basin, consistent with the accumulation of emissions underneath inversions. In the Permian Basin in Texas and New Mexico, methane columns showed maxima over regions with the highest natural gas production and were correlated with nitrogen-dioxide columns at a ratio that is consistent with results from in-situ airborne measurements. The improved detail provided by TROPOMI will likely enable the timely monitoring from space of methane emissions associated with oil and natural gas production.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Statistical Evaluation of WRF-LES Trace Gas Dispersion Using Project Prairie Grass Measurements

In recent years, new measurement systems have been deployed to monitor and quantify methane emissions from the natural gas sector. Large-eddy simulation (LES) has complemented measurement campaigns by serving as a controlled environment in which to study plume dynamics and sampling strategies. However, with few comparisons with controlled-release experiments, the accuracy of LES for modeling natural gas emissions is poorly characterized. In this paper, we evaluate LES from the Weather Research and Forecasting (WRF) Model against Project Prairie Grass campaign measurements and surface layer similarity theory. Using WRF-LES, we simulate continuous emissions from 30 near-surface trace gas sources in two stability regimes: strong convection and weak convection. We examine the impact of grid resolutions ranging from 6.25 to 52 m in the horizontal dimension on model results. We evaluate performance in a statistical framework, calculating fractional bias and conducting Welch’s t tests. WRF-LES accurately simulates observed surface concentrations at 100 m and beyond under strong convection; simulated concentrations pass t tests in this region irrespective of grid resolution. However, in weakly convective conditions with strong winds, WRF-LES substantially overpredicts concentrations—the magnitude of fractional bias often exceeds 30%, and all but one t test fails. The good performance of WRF-LES under strong convection correlates with agreement with local free convection theory and a minimal amount of parameterized turbulent kinetic energy. The poor performance under weak convection corresponds to misalignment with Monin–Obukhov similarity theory and a significant amount of parameterized turbulent kinetic energy.

17 WIND ENERGY↗