Smart Methane Emission Detection System Development Final Report

The U.S. Environmental Protection Agency (EPA) has promulgated regulations governing the detection and repair of equipment leaks that cause fugitive emissions of volatile organic compounds (VOC). These regulations are embedded in various emission standards and are generally referred to as Leak Detection and Repair (LDAR) programs. The primary method used currently in the U.S. to detect leaks is EPA Method 21.1 Method 21 requires operators to use portable instruments, typically a Flame Ionization Detector (FID) or a Photon Ionization Detector (PID), to "sniff" around the circumference of individual equipment components (e.g., valves, flanges, pump seals, etc.)2. If the detector reading (parts per million or PPM) is higher than target thresholds, the component is deemed to be leaking and it must be repaired within a certain time. Fugitive VOC emissions from a facility are calculated based upon the PPM readings (referred to as screening values or SVs) and empirical correlations between SVs and mass emission rates.2 Because the leak check is performed on each individual component basis, the implementation of a Method 21 based LDAR program is tedious, labor intensive, and prone to errors. Optical gas imaging (OGI) technology has been developed and can be used to detect VOC leaks from process equipment. The OGI technology allows operators to use a specially designed Infrared (IR) video camera to see VOC plumes leaking from components that are not visible to the naked eye. Detecting VOC leaks using OGI is more efficient than Method 21 because leak checking using OGI is visual, making detection faster, and can be performed over an area instead of component-by-component. The OGI method allows operators to detect larger leaks easily and more frequently, achieving the same environmental benefit with a lower cost. For this reason, the OGI method is also referred to as "Smart LDAR". In December 2008, U.S. EPA promulgated the "Alternative Work Practice" (AWP) rule allowing operators to use OGI for LDAR compliance.3 However, the AWP rule requires operators to continue to perform leak checks using Method 21 at least once a year. Although OGI can be very effective in detecting leaks, it does not provide a quantitative measure of leak rate. This has been one of the shortcomings of OGI from a regulatory perspective, thereby hindering its adoption as a true alternative to Method 21. This paper describes development of quantitative OGI (QOGI) technology. Existing OGI camera technology is the basis the new GOCI technology. If an OGI camera detects a leak, then, the operator can apply the new QOGI technology quantify the mass leak rate from the captured video images.

Fugitive emissions, particularly methane and Volatile Organic Compounds (VOCs), present critical environmental and economic challenges, contributing to climate change and possibly incurring regulatory penalties. A Leak Detection and Repair (LDAR) program is crucial for minimizing fugitive emissions and ensuring the safety and efficiency of these facilities. Chiyoda Corporation is a worldleading engineering company and Engineering, Procurement, and Construction (EPC) contractor for large-scale projects across the globe. In these projects, there are thousands of fugitive emission sources, and using conventional LDAR programs that rely on periodic manual inspections of individual components (e.g., EPA Method 21) has disadvantages, such as being time-consuming, incurring high labor costs, delayed leak identification, and the introduction of potential human data-entry errors. We are developing a smart LDAR technology that achieves real-time, high-accuracy leak detection through the integration of gas leak detection devices, 3D digital twin software, and robotic platforms; optimizes operational costs and reduces downtime through proactive maintenance strategies. It also ensures compliance with evolving LDAR regulations, including US-EPA, EU methane strategy, OGMP 2.0 and API 624 requirements, ensuring effective monitoring and management of methane and other fugitive emissions. The proposed technology combines three core components: (1) gas leak detection sensors—such as Optical Gas Imaging (OGI) cameras and Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensors—to detect, quantify, and visually identify gas leaks; (2) 3D digital twin software to develop an LDAR database by incorporating fugitive emission data (component type, location, concentration), enabling prioritized repair scheduling and optimized inspection routing; and (3) robotic platforms for continuous, autonomous monitoring over larger areas, collecting more data points than manual inspections and accessing challenging or hazardous zones for more accurate leak- flux quantification. The first demonstration study of the proposed technology was conducted at Chiyoda Corporation's SPERA Hydrogen demonstration plant in Japan. Results demonstrated the capabilities of the proposed technology for fugitive emission leak detection and quantification through safe and efficient monitoring without requiring proximity to potential emission sources. Future developments aim to integrate an autonomous robot instead of a manually controlled one, enabling continuous, high-precision monitoring, especially in hazardous or hard-to-access industrial environments. Our work will further enhance LDAR efficiency, improves safety, ensures regulatory compliance, and ultimately contributing to environmental sustainability and economic benefits.

Optical Gas Imaging (OGI) cameras represent an interesting tool for identifying leaking components in hydrocarbon processing and transport systems. They make it possible to see exactly where a leak originates, thereby enabling efficient leak detection and repair (LDAR) programs. The present paper reports on an OGI test campaign initiated by the Norwegian Environmental Agency (NEA), and how this campaign stimulated cross-disciplinary cooperation at an LNG plant for better control of both fugitive hydrocarbon emissions and safety-related leaks. A surprising potentially severe leak detected in the NEA campaign triggered the introduction of in-house OGI cameras at plants and refineries, and an inter-disciplinary cooperation between specialists in the environment, technical safety and operations. Some benefits of in-house OGI cameras, as well as some concerns regarding their use are presented and discussed. The general experience is that an Ex safe, i.e., rated for safe use in a combustible hydrocarbon gas atmosphere, OGI camera, represents a very valuable tool for detecting fugitive emissions as the start point for LDAR programs. An OGI camera did, however, also turn out to be a valuable tool for fire and explosion risk management, and has led to reduced downtime after leak incidents. The concerns relate to leaks seen through the OGI camera that may look overwhelming, even with concentrations well below the ignitable limits of the released gas. Based on the LNG plant experiences, it is generally recommended that specialists in the environment, technical safety, operations and teaching fields cooperate regarding the introduction and use of OGI cameras. Suggestions for training courses are also discussed.

Reducing methane emissions from oil and gas systems is a central component of US and international climate policy. Leak detection and repair (LDAR) programs using optical gas imaging (OGI)-based surveys are routinely used to mitigate fugitive emissions or leaks. Recently, new technologies and platforms such as planes, drones, and satellites promise more cost-effective mitigation than existing approaches. To be approved for use in LDAR programs, new technologies must demonstrate emissions mitigation equivalent to existing approaches. In this work, we use the FEAST modeling tool to (a) identify cost vs mitigation trade-offs that arise from using new technologies and (b) provide a framework for effective design of alternative LDAR programs. We identify several critical insights. First, LDAR programs can trade sensitivity for speed without sacrificing mitigation outcomes. Second, low sensitivity or high detection threshold technologies have an effective upper bound on achievable mitigation that is independent of the survey frequency. Third, the cost effectiveness of tiered LDAR programs using site-level detection technologies depends on their ability to distinguish leaks from routine venting. Finally, "technology equivalence" based on mitigation outcomes differs across basins and should be evaluated independently. The FEAST model will enable operators and regulators to systematically evaluate new technologies in next-generation LDAR programs.

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.

Reducing methane emissions from the oil and gas industry is a critical climate action policy tool in Canada and the US. Optical gas imaging-based leak detection and repair (LDAR) surveys are commonly used to address fugitive methane emissions or leaks. Despite widespread use, there is little empirical measurement of the effectiveness of LDAR programs at reducing long-term leakage, especially over the scale of months to years. In this study, we measure the effectiveness of LDAR surveys by quantifying emissions at 36 unconventional liquids-rich natural gas facilities in Alberta, Canada. A representative subset of these 36 facilities were visited twice by the same detection team: an initial survey and a post-repair re-survey occurring ∼0.5–2 years after the initial survey. Overall, total emissions reduced by 44% after one LDAR survey, combining a reduction in fugitive emissions of 22% and vented emissions by 47%. Furthermore, >90% of the leaks found in the initial survey were not emitting in the re-survey, suggesting high repair effectiveness. However, fugitive emissions reduced by only 22% because of new leaks that occurred between the surveys. This indicates a need for frequent, effective, and low-cost LDAR surveys to target new leaks. The large reduction in vent emissions is associated with potentially stochastic changes to tank-related emissions, which contributed ∼45% of all emissions. Our data suggest a key role for tank-specific abatement strategies as an effective way to reduce oil and gas methane emissions. Finally, mitigation policies will also benefit from more definitive classification of leaks and vents.

Reliable detection and localization of methane point sources is critical to rapidly mitigating methane emissions and avoiding the worst-case projected scenarios of climate change.  With increasingly widespread coverage and short revisit periods, satellite-based detection of methane point-sources is well-suited to identifying the largest emitters for mitigation.  To fully understand how satellite-based detection fits within multi-scale monitoring, reporting, and verification (MRV) frameworks, however, it is crucial to robustly characterize the probability of detection (POD) of active and planned instruments.  This is especially true in the upstream oil and gas sector, where measurements show highly right-skewed emissions size distributions that raise doubt on the portion of sector-wide emissions detectable from space.  While there is no shortage of statements regarding vaguely defined limits of detection (LODs) or PODs within the literature and press releases, these are often opaquely derived and, crucially, are not placed in the context of false positive rates.  Moreover, LODs based on single-blind controlled release testing are inherently problematic as prior knowledge of controlled releases precludes objective interpretation of detectability.  With ongoing efforts to improve detection sensitivities and quantification accuracies, a standardized framework for objective and transparent PODs is needed to ensure satellite-based instruments are leveraged to their full capacity. Here, we present an innovative framework to objectively quantify detection probabilities for satellite observations of methane point sources.  This generalized framework is independent of satellite resolution and precision in addition to the underlying – and potentially proprietary – method for plume segmentation.  Based in frequentist hypothesis testing, the new framework provides a POD function for satellite-specific characteristics, given a permissible false positive rate.  This latter point is critical when considering satellite techniques for use in MRV as well as leak detection and repair (LDAR) programs where nuisance detections (i.e., false positives) can render satellite approaches cost prohibitive.  We apply this new framework to methane-detecting satellite instruments in two scenarios: 1) idealized conditions of spatially uncorrelated image noise and 2) realistic conditions with spatially correlated noise derived from publicly available image data.  These analyses identify best-case PODs as a function of imagery resolution and precision and show the detrimental effect of spatially correlated noise on automated plume segmentation.  

Methane (CH 4 ) is the dominant constituent of natural gas and plays a significant role in climate change as a powerful greenhouse gas. CH 4 lingers 30 times more than CO 2 in the atmosphere and has a greater potency in trapping heat. Human activities such as Oil and Gas production, underground mining, and landfill activities contribute to 25% to 33% of global warming worldwide. Traditional methods often lack real-time and accurate monitoring capabilities. To reduce the effect of methane emissions on global warming, several research works have demonstrated the efficiency of Passive Infrared Optical Gas Imaging in methane detection and quantification, which is widely used in Leak Detection and Repair programs (LDAR) as a non-destructive and non-invasive method. The combination of Machine Learning procedures such as Faster Region Convolutional Neural Network (Faster R-CNN) along with thermal infrared cameras was proven to be effective for methane detection and quantification. However, challenges remain in accurately determining emission rates. Recent studies have also revealed that methane emissions estimates provided by agencies like the Environmental Protection Agency (EPA) are often significantly underestimated. Recently, several detection projects demonstrated that the estimates the Environment Protection Agency (EPA) provided are underestimated. This paper explores the integration of machine learning models with optical gas imaging, examining methodologies, results, and limitations in methane detection across the petroleum industry, underground mining, and landfill sectors. Future research directions include improving detection algorithms to enhance accuracy under varying environmental conditions, developing lower-cost sensor technologies for widespread deployment, and addressing regulatory challenges to ensure consistent and reliable methane reporting practices. This addition outlines promising future research areas and emphasizes key technical and regulatory challenges that must be addressed to advance methane monitoring capabilities. Keywords: Greenhouse gas emissions, methane detection and quantification, Optical Gas Imaging, Infrared Technology, Faster R-CNN. DOI : 10.7176/JETP/14-3-05 Publication date : October 30th 2024

For decades, downstream hydrocarbon production facilities have been faced with compliance with the EPA Leak Detection and Repair (LDAR) fugitive emissions program. Now the upstream business must comply with the New Source Performance Standards (NSPS) for VOC emissions for the oil and natural gas industry per EPA's proposed rules that would require that the industry reduce methane emissions. Specifically, the EPA intends on reducing fugitive methane and VOC emissions from new and modified oil and natural gas production well sites. Wellhead Emissions Management Systems, LLC of Houston, Texas has a developed a patent pending approach for the remediation of Methane and VOC emissions for gas production wellheads at a significantly lower cost than currently estimated by the EPA. Per the EPA - Regulatory Analysis Engineering Compliance Cost Estimates, the second most expensive item to comply with under the proposed regulation is fugitive emissions. The 2020 capital cost of compliance is estimated to range from $15MUSD to $22M increasing to $95M to $140M by 2025. The annualized cost of compliance is estimated to be between $29M and $47M swelling to $180M to $290M by 2025. Additional annual cost compliance estimates developed by the EPA state the cost to control wellhead fugitive emissions to be $310MUSD or $2,237 per well per year. Industry estimates for compliance have placed the cost at 0.65 cents per 1,000 SCF of gas produced. The containment and remediation system currently in field testing by Wellhead Emissions Management Systems, LLC expects to reduce the annualized cost by 30% per wellhead, while at the same time eliminating the need for immediate maintenance on wellheads when emission levels exceed the EPA NSPS allowable levels.

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.

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

NETL progress on sulfur-tolerant hydrogen membranes

According to the IEA, the oil & gas industry can achieve a 75% reduction in methane emissions with current technologies, and up to 70% at no net cost. About 62% of the emissions CO2e is coming from upstream activities. Fugitive emissions constitute the biggest single emitting source. LDAR is therefore essential in every upstream asset. In this paper, I will discuss best practices and share case studies about how LDAR can help to minimize methane emissions. Leak Detection and Repair programs have a long history with EPA Method 21 or OGI. LDAR is crucial to prioritize maintenance activities. Although some gas companies prefer OGI because of cost reasons, the more effective detection technique for all methane emissions is still using sniffing equipment PID or FID. A comparative study indicated that only 71% of the bigger leaks with more than 262 kg/year emissions are found with OGI and only 5% of the smaller leaks. Moreover, quantification of the mass leaks becomes much more accurate with sniffing equipment compared to camera screening combined with leak/no leak factors. A best in class approach is realized with an LDAR campaign that applies Risk-Based Inspection considerations in combining measuring techniques like FID/PID and OGI and frequencies referring to the probability of occurrence and consequence of emissions (source type, historical performance, stream composition, HAP, …). Another best in class practice is situated around repair activities. While in every campaign between 10 to 20% new leaks are identified, 80 to 90% of the emitting sources were leaking in previous measuring surveys and have returned. The tightening of leaking gaskets and seals provides a temporary better emission value of more than 90% reduction in only 62% of the repair attempts. Although a typical emission reduction improvement with a thorough LDAR program of 70% is achievable, more emphasis should go to in-depth problem solving to avoid recurring leaking sources. Examples include proactive mass replacement of certain gaskets or stem valve seals during turnaround activities. This continuous improvement realized with these repair activities decreases the fugitive emissions steadily. Situations, where a yearly LDAR campaign has been omitted, demonstrate a quick return to past emission figures. Executing LDAR, finetuned to the situation and outstanding legislation, is necessary to contribute to lower methane and in general GHG emissions. Based on our experience of delivering successful Leak Detection & Repair (LDAR) in the past three (3) decades, we would like to share new technologies & approaches towards detection, quantification and reduction of Methane losses, that are being implemented by various Oil Companies across the globe. Our recommendations shall be supported by actual data we have gathered from the 8,000+ fugitive emission projects we have done in the past. Also in this paper, we would like to highlight the importance of an integrated methane emission management platform (software) to ensure a transparent, auditable and manageable Methane Emission program.

The Pipeline Research Council International, Inc. (PRCI) seeks to reduce greenhouse gas (GHG) from leaks and equipment emissions from all parts of the hydrocarbon transport and storage infrastructure. Specifically, there is a need to identify and compare cost-effective technologies that can detect and quantify leaks or emissions from compressor rod packing and seals, scrubber dumps, and blowdown valves. Furthermore, regulations are increasingly prescriptive and are moving from conventional leak detection and repair (LDAR) programs to direct quantification and/or period/continuous monitoring of volume or mass flow rates as opposed to concentration-only measurements. This report summarizes recent literature, regulations, and survey results on emissions from each of the measurement categories. Data and technical literature were gathered and reviewed to enable numerical ranking of commercially available technologies or approaches that could be developed for these applications. Rubrics were developed for ranking technologies or approaches as below average (1), average (2), or above average (3) for the following categories: accuracy, flow rate range, cost, installation and maintenance, and reliability/acceptability. Based on rankings, the report then provides general guidelines and points for industry to consider along with recommendations for future research and development.

This paper reports a novel quantitative gas plume imaging tool, based on active near-infrared Backscatter Tunable Diode Laser Absorption Spectroscopy (b-TDLAS) technology, designed for upstream natural gas leak applications. The new tool integrates low-cost laser sensors with video cameras to create a highly sensitive gas plume imager that also quantifies emission rate, all in a lightweight handheld ergonomic package. It is intended to serve as a lower-cost, higherperformance, enhanced functionality replacement for traditional passive non-quantitative mid-infrared Optical Gas Imagers (OGI) which are utilized by industry to comply with natural gas infrastructure Leak Detection and Repair (LDAR) requirements. It addresses the need for reliable, robust, low-cost sensors to detect and image methane leaks, <i>and to quantify leak emission rates</i>, focusing on inspections of upstream oil and gas operations, such as well pads, compressors, and gas plants. It provides: 1) Colorized quantified images of path-integrated methane concentration. The images depict methane plumes (otherwise invisible to the eye) actively interrogated by the laser beam overlaid on a visible camera image of the background. The detection sensitivity exceeds passive OGI, thus simplifying the manual task of leak detection and location; and 2) Data and algorithms for using the quantitative information gathered by the active detection technique to deduce plume flux (i.e. methane emission rate). This key capability will enable operators to prioritize leak repairs and thereby minimize the value of lost product, as well as to quantify and minimize greenhouse gas emissions, using a tool that meets EPA LDAR imaging equipment requirements.