Methane Leak Detection Research Articles

Detection of Methane Leaks via a Drone-Based System for Sustainable Landfills and Oil and Gas Facilities: Effect of Different Variables on the Background-Noise Measurement

In recent years, thanks to the great diffusion of drone technology and the development of miniaturized sensors that can be connected to drones, in order to increase the sustainability of landfills and oil and gas facilities, interest in finding methane leaks and quantifying the relative flow has grown significantly. This operation requires the methane background concentration to be subtracted from the calculations. Therefore, in order to proceed with a right estimate of CH4 flows emitted, the possibility of correctly measuring or estimating the background level becomes crucial. The present work intends to illustrate the effects of different variables on the background-noise measurement in a drone-based system that uses a tunable diode laser absorption spectrometer (TDLAS). The methodology used is that of field testing; the data acquisition campaign consisted of the execution of 80 flights during which different flight variables (drone speed, flight altitude) were tested; the flights were repeated in different weather and climate conditions both during the same day and in different periods of the year. Different surfaces, similar to those found in landfill or natural gas sites, were also tested. In some of the field trials, a controlled methane release test was performed in order to verify how much the quantification of the methane flow can vary depending on the background level used. The results of the different field trials highlighted the best conditions under which to measure methane emissions with a TDLAS sensor in order to minimize the number of outliers: flight altitude not exceeding 15 m above ground level; the drone speed appears to have less impact on the results, however, it is optimal between 1 and 2 ms−1; a very sunny day produces much higher methane background levels than a cloudy one. The type of surface also significantly affects the measurement of background noise. Finally, tests conducted with a controlled methane release highlighted that different levels of background have a significant impact on the estimation of the methane flux emitted.

Incentives and Information in Methane Leak Detection and Repair

Incentives and Information in Methane Leak Detection and Repair

Early Detection of Pipeline Natural Gas Leakage from Hyperspectral Imaging by Vegetation Indicators and Deep Neural Networks.

The timely detection of underground natural gas (NG) leaks in pipeline transmission systems presents a promising opportunity for reducing the potential greenhouse gas (GHG) emission. However, existing techniques face notable limitations for prompt detection. This study explores the utility of Vegetation Indicators (VIs) to reflect vegetation health deterioration, thereby representing leak-induced stress. Despite the acknowledged potential of VIs, their sensitivity and separability remain understudied. In this study, we employed ground vegetation as biosensors for detecting methane emissions from underground pipelines. Hyperspectral imaging from vegetation was collected weekly at both plant and leaf scales over two months to facilitate stress detection using VIs and Deep Neural Networks (DNNs). Our findings revealed that plant pigment-related VIs, modified chlorophyll absorption reflectance index (MCARI), exhibit commendable sensitivity but limited separability in discerning stressed grasses. A NG-specialized VI, the optimized soil-adjusted vegetation index (OSAVI), demonstrates higher sensitivity and separability in early detection of methane leaks. Notably, the OSAVI proved capable of discriminating vegetation stress 21 days after methane exposure initiation. DNNs identified the methane leaks following a 3-week methane treatment with an accuracy of 98.2%. DNN results indicated an increase in visible (VIS) and a decrease in near-infrared (NIR) in spectra due to methane exposure.

A methane monitoring station siting method based on WRF-STILT and genetic algorithm

Reducing methane emissions in the oil and gas industry is a top priority for the current international community in addressing climate change. Methane emissions from the energy sector exhibit strong temporal variability and ground monitoring networks can provide time-continuous measurements of methane concentrations, enabling the rapid detection of sudden methane leaks in the oil and gas industry. Therefore, identifying specific locations within oil fields to establish a cost-effective and reliable methane monitoring ground network is an urgent and significant task. In response to this challenge, this study proposes a technical workflow that, utilizing emission inventories, atmospheric transport models, and intelligent computing techniques, automatically determines the optimal locations for monitoring stations based on the input quantity of monitoring sites. This methodology can automatically and quantitatively assess the observational effectiveness of the monitoring network. The effectiveness of the proposed technical workflow is demonstrated using the Shengli Oilfield, the second-largest oil and gas extraction base in China, as a case study. We found that the Genetic Algorithm can help find the optimum locations effectively. Besides, the overall observation effectiveness grew from 1.7 to 5.6 when the number of site increased from 1 to 9. However, the growth decreased with the increasing site number. Such a technology can assist the oil and gas industry in better monitoring methane emissions resulting from oil and gas extraction.

Sensor Data Analytics for Optimized Methane Leak Detection and Mitigation

Sensor Data Analytics for Optimized Methane Leak Detection and Mitigation

Large-Scale Controlled Experiment Demonstrates Effectiveness of Methane Leak Detection and Repair Programs at Oil and Gas Facilities.

Most jurisdictions around the globe use leak detection and repair (LDAR) programs to find and fix methane leaks from oil and gas operations. In this work, we empirically evaluate the efficacy of LDAR programs using a large-scale, bottom-up, randomized controlled field experiment across ∼200 oil and gas sites in Red Deer, Canada. We find that tanks are the single largest source of emissions, contributing to nearly 60% of the total emissions. The average number of leaks at treatment sites that underwent repair reduced by ∼50% compared to the control sites. Although control sites did not see a reduction in the number of leaks, emissions reduced by approximately 36%, suggesting potential impact of routine maintenance activities to find and fix large leaks. By tracking tags on leaking equipment over time, we find a high degree of persistence; leaks that are repaired remain fixed in follow-up surveys, while non-repaired leaks remain emitting at a similar rate, suggesting that any increase in observed leak emissions following LDAR surveys are likely from new leaks. Our results show that a focus on equipment and sites that are prone to high emissions, such as tanks and oil sites, is key to cost-effective mitigation.

Innovative Thinking Drives Emissions Mitigation

_ The oil and gas industry walks a fine line between providing the energy supplies demanded by a growing global population and addressing the challenge of reducing greenhouse gas emissions, mainly methane. While the industry has made gains in many areas, critics have flagged concerns that the current pace of emissions mitigation needs to be faster and called for acceleration of the process. According to the International Energy Agency (IEA), methane is responsible for approximately 30% of the rise in global temperatures since the Industrial Revolution, making it the second-largest contributor to climate change behind carbon dioxide. Methane is released during oil and gas production processes, and the industry accounts for about a quarter of the global anthropogenic methane emitted into the atmosphere. With increasing recognition of their negative environmental impacts, these emissions are now under greater scrutiny by governments worldwide as they strive toward cleaner power generation sources. As a result, energy companies continue to find ways to effectively manage and reduce their methane emissions to remain compliant with new regulations. According to nonprofit organizations Clean Air Task Force and Ceres, industry efforts are delivering results. An analysis assessed the production-based emissions of more than 300 US producers, finding that the methane emissions intensity of natural gas production and the greenhouse-gas emissions intensity of oil and gas production vary dramatically across companies. The intensity of methane and greenhouse-gas emissions from the oil and gas sector declined 28% and 30%, respectively, between 2019 and 2021 among the largest producers in the country, the report said. However, according to the IEA, overall progress could have been faster. In a special report, the agency looked at the investment required to deliver a sharp reduction in emissions by 2030, finding that if the industry spent $75 billion—about 2% of its 2022 net income—to curb methane emissions through 2030, it would keep the sector on a pathway to achieve net-zero emissions by 2050. The agency also reported that methane abatement is one of the most cost-effective measures to cut emissions and would generate about $45 billion from the sale of captured methane (Fig. 1). Leak Detection Comparisons Promising advances in technology have improved methane measurement and leak detection. Traditionally a laborious and error-prone task, leak-detection technologies are undergoing a revolution. Drone technology, satellite surveillance, and infrared cameras are emerging as powerful tools in the industry’s arsenal to pinpoint leaks and fix them swiftly. Companies like Shell and BP have committed to routine checks via drones and cameras to spot leaks more accurately and promptly. By employing these technologies, the industry can reduce its environmental footprint and save valuable natural resources, transforming “waste” methane into a valuable energy source. ExxonMobil deployed new technology to expand measurement and mitigation of methane emissions and reduced methane emissions intensity from operated assets by more than 40% as of year-end 2021 vs. 2016 levels in line with greenhouse gas emission reduction plans. In its 2023 Advancing Climate Solutions Progress Report, the supermajor also said that it increased the amount it intends to invest through 2027 on lower-emission initiatives to approximately $17 billion, up by nearly 15%.

Daily detection and quantification of methane leaks using Sentinel-3: a tiered satellite observation approach with Sentinel-2 and Sentinel-5p

The twin Sentinel-3 satellites have multi-band radiometers which observe in methane-sensitive shortwave infrared bands with daily global coverage and 500 m ground pixel resolution. We investigate the methane observation capability of Sentinel-3 and how its coverage-resolution combination fits between Sentinel-5p and Sentinel-2 within a tiered observation approach for methane leak monitoring. Sentinel-5p measures methane with high precision and daily global coverage, allowing worldwide leak detection but with a coarse spatial resolution of 7 km × 5.5 km. The Sentinel-2 twin satellites have multi-band instruments that can identify source locations of major leaks (> 1 t/h) with their methane observations of 20 m resolution under favorable observational conditions, but these satellites lack daily global coverage.We show that methane enhancements can be retrieved from the shortwave infrared band measurements of Sentinel-3. We report the lowest emission detections by Sentinel-3 in the 8-20 t/h range, depending on location and wind conditions. We demonstrate Sentinel-3's capability of identification and monitoring of methane leaks using two case studies. Near Moscow, Sentinel-3 shows that two major short-term leaks, separated by 30 km, occurred simultaneously at a gas pipeline and appear as a single methane plume in Sentinel-5p data. For another Sentinel-5p leak detection near the Hassi Messaoud oil/gas field in Algeria, Sentinel-3 identifies the leaking facility emitting continuously for 6 days, and Sentinel-2 pinpoints the source of the leak at an oil/gas well. Sentinel-2 and Sentinel-3 also show the 6-day leak was followed by a four-month period of burning of the leaking gas, suggesting a gas well blowout to be the cause of the leak. We find similar source rate quantifications from plume detections by Sentinel-3 and Sentinel-2 for these leaks, demonstrating Sentinel-3's utility for emission quantification. We show that zooming in with Sentinel-3 and Sentinel-2 in synergy allows precise identification, quantification, and monitoring of the sources corresponding to methane plumes observed in Sentinel-5p's global scans.

Optical Methods of Methane Detection.

Methane is the most frequently analyzed gas with different concentrations ranging from single ppm or ppb to 100%. There are a wide range of applications for gas sensors including urban uses, industrial uses, rural measurements, and environment monitoring. The most important applications include the measurement of anthropogenic greenhouse gases in the atmosphere and methane leak detection. In this review, we discuss common optical methods used for detecting methane such as non-dispersive infrared (NIR) technology, direct tunable diode spectroscopy (TDLS), cavity ring-down spectroscopy (CRDS), cavity-enhanced absorption spectroscopy (CEAS), lidar techniques, and laser photoacoustic spectroscopy. We also present our own designs of laser methane analyzers for various applications (DIAL, TDLS, NIR).

Incentives and Information in Methane Leak Detection and Repair

Incentives and Information in Methane Leak Detection and Repair

Mapping methane point emissions with the PRISMA spaceborne imaging spectrometer

The detection and repairment of methane leaks from fossil fuel production activities have been identified as a key climate change mitigation strategy. Several types of optical satellite sensors have recently shown potential to support this task. Spaceborne imaging spectrometers measuring in the 400–2500 nm spectral range belong to this group. These instruments measure the solar radiation reflected by the Earth in hundreds of spectral channels with a typical spectral resolution of 10 nm and a spatial resolution of 30 m. The PRISMA mission from the Italian Space Agency is the first system of this type providing data openly to the international scientific community. In this work, we evaluate the potential of PRISMA to map methane point emissions, which are typical for fossil fuel production activities. Our retrieval of methane concentration enhancements is based on a matched-filter based algorithm applied to PRISMA spectra in the 2300 nm shortwave infrared spectral region. We perform a simulation-based sensitivity analysis to assess the retrieval performance for different sites. We find that surface brightness and homogeneity are major drivers for the detection and quantification of methane plumes with PRISMA, with retrieval precision errors ranging from 61 to 197 parts-per-billion in the evaluated images. The potential of PRISMA for methane mapping is further illustrated by real plume detections at different methane hotspot regions, including oil and gas extraction fields in Algeria, Turkmenistan, and the USA (Permian Basin), and coal mines in the Shanxi region in China. Our study reports several important findings regarding the potential and limitations of PRISMA for methane mapping, most of which can be extrapolated to upcoming satellite imaging spectroscopy missions.

VideoGasNet: Deep learning for natural gas methane leak classification using an infrared camera

Mitigating methane leakage from the natural gas system has become an increasing concern for climate change. Efficacious methane leak detection and classification can make mitigation more efficient and cost effective. Optical gas imaging (OGI) is widely used for the purpose of leak detection, but it does not directly provide quantitative detection results and leak sizes. In this study, we consider methane leak size classification as a video classification problem and develop deep learning models to classify the videos by leak volume. Firstly we collected the first methane leak video dataset - GasVid, which has ∼0.7 M frames of labeled videos of methane leaks from different leaking equipment, covering a wide range of leak sizes (5.3–2051.6 gCH4/h) and imaging distances (4.6–15.6 m). Secondly, we studied three deep learning algorithms, including 2D Convolutional Neural Networks (CNN) model, 3D CNN and the Convolutional Long Short Term Memory (ConvLSTM). We find that 3D CNN is the most accurate and robust architecture, and we name it VideoGasNet. The leak/non-leak binary detection accuracy approaches nearly 100%, and the highest small-medium-large classification accuracy is 78.2% with our 3D CNN network. In eight-class classification, accuracy drops to 39.1%, as this is a more challenging task. In summary, VideoGasNet greatly extends the capabilities of IR camera-based leak monitoring system from leak detection only to automated leak classification, and shows high accuracy for binary and three-class classification.

Methane leak detection by tunable laser spectroscopy and mid-infrared imaging.

Tunable laser spectroscopy (TLS) combined with mid-infrared imaging is a powerful tool for a sensitive and quantitative visualization of gas leaks. This work deals with standoff methane leak detection within 2 m by an interband cascade laser (3270 nm wavelength) and an infrared camera. The concept demonstrates visualization of methane leakage rates down to 2 ml/min by images and sequences at frame rates up to 125 Hz. The gas plume and leak can be localized and quantified within a single image by direct absorption spectroscopy (DAS). The HITRAN database allows a calibration-free, pixelwise determination of the concentration in ppm*m. The active optical imaging concept showed pixelwise sensitivities around 1 ppm*m.

EPA Methane Emission Controls, Obama vs Trump vs Biden: What Needs to Be Fixed and What Should be Left Alone

On 13 August 2020, the U.S. Environmental Protection Agency promulgated its final rule amending the New Source Performance Standards (NSPS) for the Oil and Natural Gas Sector. Both adherents and detractors of President Donald J. Trump saw the new rule as contributing to the President’s deregulatory agenda, taking an important step toward American dominance in fossil fuels (desirable or not), and winning yet another battle in the war on the environmental protection legacy of President Barack Obama. This simple story line is compelling, but when the details are examined, it is found that the changes were in some ways more important, and in other ways less important than advertised by supporters and opponents. Moreover, some of the changes have damaged positions Trump championed, while others have the potential to substantially improve the environmental performance of the oil and gas industry. Most importantly, some major sources of methane emissions have been inadequately dealt with across both administrations. Therefore the Biden administration EPA, in re-examining NSPS, should take care to discard the mistakes made by the Trump administration, retain the improvements, and move forward on a number of new fronts. Topics explored include redundancy of methane and volatile organic compound leak detection, regulation of older and low production facilities, abandoned wells, pneumatic controllers, oil storage tanks, routine flaring, malfunctioning and unlit flares, gathering pipelines, and EPA certification of new methane emission detection technology.

An enhanced procedure for urban mobile methane leak detection

Leaked methane from natural gas distribution pipelines is a significant human and environmental health problem in urban areas. To assess this risk, urban mobile methane leak surveys were conducted, using innovative methodology, on the streets of Hartford, Danbury, and New London, Connecticut, in March 2019. The Hartford survey was done to determine if results from a 2016 survey (Keyes et al., 2019) were persistent, and surveys in additional towns were done to determine if similar findings could be made using an identical approach. Results show that Hartford continues to be problematic, with approximately 3.4 leaks per road mile observed in 2016 and 4.3 leaks per mile estimated in 2019, similar to that previously found in Boston, Massachusetts (Phillips et al., 2013). A preliminary estimate of methane leaks in Hartford is 0.86 metric tonnes per day (or 313 metric tonnes per year), equivalent to 42,840 cubic feet per day of natural gas, and a daily gas consumption of approximately 214 U.S. households. Moreover, the surveys and analyses done for Danbury and New London also reveal problematic leaks, particularly for Danbury with an estimated 3.6 leaks per mile. Although road miles covered in New London were more limited, the survey revealed leak-prone areas, albeit with a range of methane readings lower than those in Hartford and Danbury. Data collection methods for all studies is first reported here and are readily transferable to similar urban settings. This work demonstrates the actionable value that can be gained from data-driven evaluations of urban pipeline performance, and if supplemented with a map of leak-prone pipe geo-location, and information on pipeline operating pressures, will provide a spatial database facilitating proactive repair and replacement of leak-prone urban pipes, a considerable improvement compared to reactive mitigation of human-reported leaks. While this work pertains to the selected urban towns in the Northeast, it exemplifies issues and opportunities nationwide in the United States.

Machine vision for natural gas methane emissions detection using an infrared camera

In a climate-constrained world, it is crucial to reduce natural gas methane emissions, which can potentially offset the climate benefits of replacing coal with gas. Optical gas imaging (OGI) is a widely-used method to detect methane leaks, but is labor-intensive and cannot provide leak detection results without operators’ judgment. In this paper, we develop a computer vision approach for OGI-based leak detection using convolutional neural networks (CNN) trained on methane leak images to enable automatic detection. First, we collect ∼1 M frames of labeled videos of methane leaks from different leaking equipment, covering a wide range of leak sizes (5.3–2051.6 g CH4/h) and imaging distances (4.6–15.6 m). Second, we examine different background subtraction methods to extract the methane plume in the foreground. Third, we then test three CNN model variants, collectively called GasNet, to detect plumes in videos. We assess the ability of GasNet to perform leak detection by comparing it to a baseline method that uses an optical-flow based change detection algorithm. We explore the sensitivity of results to the CNN structure, with a moderate-complexity variant performing best across distances. The generated detection probability curves show that the detection accuracy (fraction of leak and non-leak images correctly identified by the algorithm) can reach as high as 99%, the overall detection accuracy can exceed 95% across all leak sizes and imaging distances. Binary detection accuracy exceeds 97% for large leaks (∼710 g CH4/h) imaged closely (∼5–7 m). The GasNet-based computer vision approach could be deployed in OGI surveys for automatic vigilance of methane leak detection with high accuracy in the real world.

"Good versus Good Enough?" Empirical Tests of Methane Leak Detection Sensitivity of a Commercial Infrared Camera.

Methane, a key component of natural gas, is a potent greenhouse gas. A key feature of recent methane mitigation policies is the use of periodic leak detection surveys, typically done with optical gas imaging (OGI) technologies. The most common OGI technology is an infrared camera. In this work, we experimentally develop detection probability curves for OGI-based methane leak detection under different environmental and imaging conditions. Controlled single blind leak detection tests show that the median detection limit (50% detection likelihood) for FLIR-camera based OGI technology is about 20 g CH4/h at an imaging distance of 6 m, an order of magnitude higher than previously reported estimates of 1.4 g CH4/h. Furthermore, we show that median and 90% detection likelihood limit follows a power-law relationship with imaging distance. Finally, we demonstrate that real-world marginal effectiveness of methane mitigation through periodic surveys approaches zero as leak detection sensitivity improves. For example, a median detection limit of 100 g CH4/h is sufficient to detect the maximum amount of leakage that is possible through periodic surveys. Policy makers should take note of these limits while designing equivalence metrics for next-generation leak detection technologies that can trade sensitivity for cost without affecting mitigation priorities.

Ubiquitous Low-Cost Functionalized Multi-Walled Carbon Nanotube Sensors for Distributed Methane Leak Detection

This paper presents a highly sensitive, energy efficient, and low-cost distributed methane (CH4) sensor system (DMSS) for continuous monitoring, detection, and localization of CH4 leaks in natural gas infrastructure, such as transmission and distribution pipelines, wells, and production pads. The CH4 sensing element, a key component of the DMSS, consists of a metal–oxide nanocrystal functionalized multi-walled carbon nanotube mesh, which, in comparison with the existing literature, shows stronger relative resistance change while interacting with lower parts per million concentration of CH4. A Gaussian plume triangulation algorithm has been developed for the DMSS. Given a geometric model of the surrounding environment, the algorithm can precisely detect and localize a CH4 leak as well as estimate its mass emission rate. A UV-based surface recovery technique making the sensor recover ten times faster than the reported ones is presented for the DMSS. A control algorithm based on the UV-accelerated recovery is developed, which facilitates faster leak detection.

Are Optical Gas Imaging Technologies Effective For Methane Leak Detection?

Concerns over mitigating methane leakage from the natural gas system have become ever more prominent in recent years. Recently, the U.S. Environmental Protection Agency proposed regulations requiring use of optical gas imaging (OGI) technologies to identify and repair leaks. In this work, we develop an open-source predictive model to accurately simulate the most common OGI technology, passive infrared (IR) imaging. The model accurately reproduces IR images of controlled methane release field experiments as well as reported minimum detection limits. We show that imaging distance is the most important parameter affecting IR detection effectiveness. In a simulated well-site, over 80% of emissions can be detected from an imaging distance of 10 m. Also, the presence of "superemitters" greatly enhance the effectiveness of IR leak detection. The minimum detectable limits of this technology can be used to selectively target "superemitters", thereby providing a method for approximate leak-rate quantification. In addition, model results show that imaging backdrop controls IR imaging effectiveness: land-based detection against sky or low-emissivity backgrounds have higher detection efficiency compared to aerial measurements. Finally, we show that minimum IR detection thresholds can be significantly lower for gas compositions that include a significant fraction nonmethane hydrocarbons.

Comparing Natural Gas Leakage Detection Technologies Using an Open-Source "Virtual Gas Field" Simulator.

We present a tool for modeling the performance of methane leak detection and repair programs that can be used to evaluate the effectiveness of detection technologies and proposed mitigation policies. The tool uses a two-state Markov model to simulate the evolution of methane leakage from an artificial natural gas field. Leaks are created stochastically, drawing from the current understanding of the frequency and size distributions at production facilities. Various leak detection and repair programs can be simulated to determine the rate at which each would identify and repair leaks. Integrating the methane leakage over time enables a meaningful comparison between technologies, using both economic and environmental metrics. We simulate four existing or proposed detection technologies: flame ionization detection, manual infrared camera, automated infrared drone, and distributed detectors. Comparing these four technologies, we found that over 80% of simulated leakage could be mitigated with a positive net present value, although the maximum benefit is realized by selectively targeting larger leaks. Our results show that low-cost leak detection programs can rely on high-cost technology, as long as it is applied in a way that allows for rapid detection of large leaks. Any strategy to reduce leakage should require a careful consideration of the differences between low-cost technologies and low-cost programs.