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

Neural Networks Based on Synthesized Training Data for the Automated Detection of Chemical Plumes in Passive Infrared Multispectral Images

Automated detection of volatile organic compounds in the atmosphere can be achieved by applying pattern recognition analysis to passive infrared (IR) multispectral remote sensing data. However, obtaining analyte-active training data through field experiments is time-consuming and expensive. To address this issue, methodology has been developed for simulating radiance profiles acquired using a multispectral IR line-scanner mounted in a downward-looking position on a fixed-wing aircraft. The simulation strategy used Planck's radiation law and a radiometric model along with the laboratory spectrum of the target compound to compute the upwelling IR background radiance with the presence of the analyte within the instrumental field-of-view. By combining the simulated analyte-active radiances and field-collected analyte-inactive radiances, a synthetic training dataset was constructed. A backpropagation neural network was employed to build classifiers with the synthetic training dataset. Employing methanol as the target compound, the performance of the classifiers was evaluated with field-collected data from airborne surveys at two test fields.

The Role of Emission Size Distribution on the Efficacy of New Technologies to Reduce Methane Emissions from the Oil and Gas Sector

Methane emissions from oil and gas operations exhibit skewed distributions. New technologies such as aerial-based leak detection surveys promise cost-effective detection of large emitters (greater than 10 kg/h). Recent policies such as the US Environmental Protection Agency (EPA) methane rule that allow the use of new technologies as part of leak detection and repair (LDAR) programs require a demonstration of equivalence with existing optical gas imaging (OGI) based LDAR programs. In this work, we illustrate the impact of emission size distribution on the equivalency condition between the OGI and site-wide survey technologies. Emission size distributions compiled from aerial measurements include significantly more emitters between 1 and 10 kg/h and lower average emission rates for large emitters compared to the emission distribution in the EPA rule. As a result, we find that equivalence may be achieved at lower site-wide survey frequencies when using technologies with detection thresholds below 10 kg/h, compared to the EPA rule. However, equivalence cannot be achieved with a detection threshold of 30 kg/h at any survey frequency, because most emitters across most US basins exhibit emission rates below 30 kg/h. We find that equivalence is a complex tradeoff among technology choice, design of LDAR programs, and survey frequency that can have more than one unique solution set.

Tiered Leak Detection and Repair Programs at Simulated Oil and Gas Production Facilities: Increasing Emission Reduction by Targeting High-Emitting Sources

Distributions of methane emission rates originating from oil and gas production facilities are highly skewed and span 6-8 orders of magnitude. Traditional leak detection and repair programs have relied on surveys with handheld detectors at intervals of 2 to 4 times a year to find and fix emissions; however, this approach may lead unintended emissions to be active for the same interval independently of their magnitude. In addition, manual surveys are labor intensive. Novel methane detection technologies offer opportunities to further reduce emissions by quickly detecting the high-emitters, which account for a disproportionate fraction of total emissions. In this work, combinations of methane detection technologies with a focus of targeting high-emitting sources were simulated in a tiered approach for facilities representative of the Permian Basin, a region with skewed emission rates where emissions above 100 kg/h account for 40-80% of production site-wide total emissions, which include sensors on satellites, aircraft, continuous monitors, and optical gas imaging (OGI) cameras, with variations on survey frequency, detection thresholds, and repair times. Results show that strategies that quickly detect and fix high-emitting sources while decreasing the frequency of OGI inspections, which find the smaller emissions, achieve higher reductions than quarterly OGI and, in some cases, reduce emissions further than monthly OGI.

Oriented External Electric Fields Regurating the Reaction Mechanism of CH4 Oxidation Catalyzed by Fe(IV)-Oxo-Corrolazine: Insight from Density Functional Calculations

Methane is the simplest alkane and can be used as an alternative energy source for oil and coal, but the greenhouse effect caused by its leakage into the air is not negligible, and its conversion into liquid methanol not only facilitates transportation, but also contributes to carbon neutrality. In order to find an efficient method for converting methane to methanol, CH₄ oxidation catalyzed by Fe(IV)-Oxo-corrolazine (Fe(IV)-Oxo-Cz) and its reaction mechanism regulation by oriented external electric fields (OEEFs) are systematically studied by density functional calculations. The calculations show that Fe(IV)-Oxo-Cz can abstract one H atom from CH₄ to form the intermediate with OH group connecting on the corrolazine ring, with the energy barrier of 25.44 kcal mol⁻¹. And then the product methanol is formed through the following rebound reaction. Moreover, the energy barrier can be reduced to 20.72 kcal mol⁻¹ through a two-state reaction pathway. Furthermore, the effect of OEEFs on the reaction is investigated. We found that OEEFs can effectively regulate the reaction by adjusting the stability of the reactant and the transition state through the interaction of electric field-molecular dipole moment. When the electric field is negative, the energy barrier of the reaction decreases with the increase of electric intensity. Moreover, the OEEF aligned along the intrinsic Fe‒O reaction axis can effectively regulate the ability of forming the OH on the corrolazine ring by adjusting the charges of O and H atoms. When the electric field intensity is −0.010 a.u., the OH can be directly rebounded to the CH₃· before it is connecting on the corrolazine ring, thus forming the product directly from the transition state without passing through the intermediate with only an energy barrier of 17.34 kcal mol⁻¹, which greatly improves the selectivity of the reaction.

Advanced Leak Detection and Quantification of Methane Emissions Using sUAS

Detecting and quantifying methane emissions is gaining an increasingly vital role in mitigating emissions for the oil and gas industry through early detection and repair and will aide our understanding of how emissions in natural ecosystems are playing a role in the global carbon cycle and its impact on the climate. Traditional methods of measuring and quantifying emissions utilize chamber methods, bagging individual equipment, or require the release of a tracer gas. Advanced leak detection techniques have been developed over the past few years, utilizing technologies, such as optical gas imaging, mobile surveyors equipped with sensitive cavity ring down spectroscopy (CRDS), and manned aircraft and satellite approaches. More recently, sUAS-based approaches have been developed to provide, in some ways, cheaper alternatives that also offer sensing advantages to traditional methods, including not being constrained to roadways and being able to access class G airspace (0–400 ft) where manned aviation cannot travel. This work looks at reviewing methods of quantifying methane emissions that can be, or are, carried out using small unmanned aircraft systems (sUAS) as well as traditional methods to provide a clear comparison for future practitioners. This includes the current limitations, capabilities, assumptions, and survey details. The suggested technique for LDAQ depends on the desired accuracy and is a function of the survey time and survey distance. Based on the complexity and precision, the most promising sUAS methods are the near-field Gaussian plume inversion (NGI) and the vertical flux plane (VFP), which have comparable accuracy to those found in conventional state-of-the-art methods.

New Technologies Can Cost Effectively Reduce Oil and Gas Methane Emissions, but Policies Will Require Careful Design to Establish Mitigation Equivalence

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.

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.

Projecting the Temporal Evolution of Methane Emissions from Oil and Gas Production Sites

Many recent studies have reported methane emissions from oil and gas production regions, often reporting results as a methane emission intensity (methane emitted as a percentage of natural gas produced or methane produced). Almost all of these studies have been instantaneous snapshots of methane emissions; however, total methane emissions from a production site and the methane emission intensity would be expected to evolve over time. A detailed site-level methane emission estimation model is used to estimate the temporal evolution of methane emissions and the methane emission intensity for a variety of well configurations with and without emission mitigation measures in place. The general pattern predicted is that total emissions decrease over time as production declines. Methane emission intensity shows complex behavior because production-dependent emissions decline at different rates and some emissions do not decline over time. Prototypical uncontrolled wet gas wells can have approximately half of their emissions over a 10 year period occur in the first year; instantaneous wellsite methane emission intensities range over a factor of 3 (0.62-2.00%) in the same period, with a 10 year production weighted-average lifecycle methane emission intensity of 0.79%. Including emission control in the form of a flare can decrease the average lifecycle methane emission intensity to 0.23%. Emissions from liquid unloadings, which are observed in subsets of wells, can increase the lifecycle methane emission intensity by up to a factor of 2-3, between 1.2 and 2.3%, depending on the characteristics of the unloadings. Emissions from well completion flowbacks raise the average lifecycle methane emission intensity from 0.79 to 0.81% for flowbacks with emission controls; for flowbacks with uncontrolled emissions, lifecycle methane emissions increase to 1.26%. Dry gas and oil wells show qualitatively similar temporal behavior but different absolute emission rates.

Aerially guided leak detection and repair: A pilot field study for evaluating the potential of methane emission detection and cost-effectiveness

Novel aerial methane (CH₄) detection technologies were used in this study to identify anomalously high-emitting oil and gas (O&G) facilities and to guide ground-based "leak detection and repair" (LDAR) teams. This approach has the potential to enable a rapid and effective inspection of O&G facilities under voluntary or regulatory LDAR programs to identify and mitigate anomalously large CH₄ emissions from a disproportionately small number of facilities. This is the first study of which the authors are aware to deploy, evaluate, and compare the CH₄ detection volumes and cost-effectiveness of aerially guided and purely ground-based LDAR techniques. Two aerial methods, the Kairos Aerospace infrared CH₄ column imaging and the Scientific Aviation in situ aircraft CH₄ mole fraction measurements, were tested during a 2-week period in the Fayetteville Shale region contemporaneously with conventional ground-based LDAR. We show that aerially guided LDAR can be at least as cost-effective as ground-based LDAR, but several variable parameters were identified that strongly affect cost-effectiveness and which require field research and improvements beyond this pilot study. These parameters include (i) CH₄ minimum dectectable limit of aerial technologies, (ii) emission rate size distributions of sources, (iii) remote distinction of fixable versus nonfixable CH₄ sources ("leaks" vs. CH₄ emissions occurring by design), and (iv) the fraction of fixable sources to total CH₄ emissions. Suggestions for future study design are provided. Implications: Mitigation of methane leaks from existing oil and gas operations currently relies on on-site inspections of all applicable facilities at a prescribed frequency. This approach is labor- and cost-intensive, especially because a majority of oil and gas-related methane emissions originate from a disproportionately small number of facilities and components. We show for the first time in real-world conditions how aerial methane measurements can identify anomalously high-emitting facilities to enable a rapid, focused, and directed ground inspection of these facilities. The aerially guided approach can be more cost-effective than current practices, especially when implementing the aircraft deployment improvements discussed here.

Natural Gas Fugitive Leak Detection Using an Unmanned Aerial Vehicle: Localization and Quantification of Emission Rate

We describe a set of methods for locating and quantifying natural gas leaks using a small unmanned aerial system equipped with a path-integrated methane sensor. The algorithms are developed as part of a system to enable the continuous monitoring of methane, supported by a series of over 200 methane release trials covering 51 release location and flow rate combinations. The system was found throughout the trials to reliably distinguish between cases with and without a methane release down to 2 standard cubic feet per hour (0.011 g/s). Among several methods evaluated for horizontal localization, the location corresponding to the maximum path-integrated methane reading performed best with a mean absolute error of 1.2 m if the results from several flights are spatially averaged. Additionally, a method of rotating the data around the estimated leak location according to the wind is developed, with the leak magnitude calculated from the average crosswind integrated flux in the region near the source location. The system is initially applied at the well pad scale (100–1000 m2 area). Validation of these methods is presented including tests with unknown leak locations. Sources of error, including GPS uncertainty, meteorological variables, data averaging, and flight pattern coverage, are discussed. The techniques described here are important for surveys of small facilities where the scales for dispersion-based approaches are not readily applicable.

"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.

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.