Advanced Leak Detection and Quantification of Methane Emissions Using sUAS

Abstract

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.

A Framework to Survey a Region for Gas Leaks Using an Unmanned Aerial Vehicle

“…However, monitoring the entire production, transmission, and/or distribution system with stationary sensors requires a large quantity of sensors. Furthermore, sensors close to the ground may fail to detect leaks when heat and wind disperse the gas to higher altitudes, being out of reach of sensors [14], [35]. For such reasons, stationary sensor solutions are typically deployed close to gas production facilities or high risk areas [5], [28].…”

“…UAVs allow lower flying altitudes (e.g. Class G airspace not available to manned aircraft [14]) and authors have reported the ability to detect with LIDAR sensors leak rates as low as 5 kg/hr when flying the UAV at altitudes ranging from 3m to 15m above the leak [53] and 13 kg/hr when flying at an altitude of 50m [31].…”

“…Answering these questions is not easy because detection of high gas concentration can be highly intermittent due to the coupling between the filamentous nature of the gas plume and atmospheric turbulence [12], [13]. A high methane concentration measured by the UAV only indicates that the UAV has encountered a gas filament and inferring the upwind gas leak location from such a measurement is not trivial [9], [10], [14].…”

Recent Developments in Sensor Technologies for Enabling the Hydrogen Economy

“…5d). 112,132,[166][167][168][169] Black box artificial neural networks without a physical atmospheric model have also successfully localized the emissions for gases from data collected with an array of sensors. 170,171 These methods have been applied for methane emissions monitoring but are expected to be applicable for hydrogen or hydrogen-methane blends as well.…”

Comparison of Machine Learning Algorithms for Natural Gas Identification with Mixed Potential Electrochemical Sensor Arrays

“…33,34 Combining ML algorithms with unmanned aerial and ground vehicles has also been shown as a promising method to survey natural gas infrastructure for leaks where large areas in remote locations need to be covered. [35][36][37] Other surveys have compared different machine learning techniques z E-mail: lktsui@unm.edu ECS Sensors Plus, 2023 2 011402 applied to other types of gas sensors 38,39 and specific studies such as Tsitron et al 40 demonstrate the application of a single machine learning technique such as Bayesian decoding to MPES devices. We have previously demonstrated the effectiveness of artificial neural networks for gas mixture identification in the context of natural gas emissions 18 and automotive emissions.…”