Understanding methane levels in the atmosphere and the sources contributing to it is a common topic of discussion as we consider our energy future. Many efforts are focused on improving our understanding of emissions from the production, processing, transportation, and use of oil and natural gas. These efforts often involve “measuring” methane emissions to get a more precise understanding rather than relying on estimates from emissions factors. It’s important to understand the limitations of the term “measurement” in the context of the tools available and the field conditions experienced.
Detecting, assessing, and quantifying methane emissions in the field does not occur with the precision that may be implied by the term measurement.
Quantification vs. Measurement
My wife is a quilter. I have watched her go through an exacting process in which accuracy of measurement is critical to produce beautifully complex patterns. Among the many tools on the quilters table are a variety of tapes, rulers and templates, each of which provide a means of obtaining an exact measurement. Those tools can be relied upon because they are calibrated against a standard and provide a repeatable, consistent result, regardless of which tool is selected. As an engineer, I appreciate the importance of measurement as a process that is accurate, consistent, and repeatable.
Accurately determining the volume of methane emissions in an operating environment can be challenging due to the variety of sources and the variability of conditions in which they occur. Factors affecting accuracy include frequent changes in wind direction and speed, accessibility of the emissions source and variability in emissions rate that can dramatically affect quantification depending on the time at which the event is observed. The tools used to evaluate an emissions event have error bars for a single “measurement” ranging from +/- 17% at the most accurate to +/- 70% for the types of events that occur in the field. Using the quilter as an example, with that error bar a one-inch block might, on average, range from less than a half-inch to more than an inch and a half, creating chaos when precision is needed. Fortunately, we don’t need this level of precision in the evaluation of methane emissions to achieve the goal of eliminating them, but we also need to understand the lack of precision with which we are working.
Efforts to minimize error include the gathering of localized weather observations, increased sampling frequency, optimizing the distance from the emissions source and refining algorithms that process data. Selection of the sensor type for specific field conditions is also an important consideration. The resulting science-based estimates are better characterized as quantification rather than measurement, recognizing they are a more accurate and scientific approach than an emissions factor but fail to provide a consistent, or repeatable measurement of an emissions event.
The resulting science-based estimates are better characterized as quantification....
Tools for Detection and Quantification
Methane detection and quantification tools typically consist of a sensor that detects methane in the atmosphere, additional data collection (such as temperature, humidity, and wind conditions), and proprietary algorithms that process the data to provide quantification through inference. There are various types of sensors that can be used for methane detection, including Tunable Diode Laser Absorption Spectroscopy (TDLAS), Metal Oxide Sensors (MOX), Short Wave Infrared (SWIR), Light Detection and Ranging (LiDAR), Optical Gas Imaging (OGI), Cavity Ring Down Spectroscopy, and others. Each sensor type has limitations that can introduce error. The need for additional data and the quality of proprietary analytics also impacts the results.
Each sensor type has strengths and limitations, and can be deployed in different ways, such as through handheld instruments, mobile platforms like vehicles, drones, or satellites, or as fixed sensors. Some sensors require external light, while others have an internal light source. Some sensors require wind to function properly, while others may not work in windy conditions. Background conditions can also affect the accuracy of optical systems. Leading technology providers are working to improve the accuracy and reliability of methane detection tools through more frequent observations, improved light sources, enhanced signal processing, and refinement of proprietary algorithms. These efforts aim to increase the probability of detection at lower detection limits while considering economic factors; however, even with improvement the results will not achieve the level of accuracy and repeatability characterizing measurement.
The quilter works in a controlled environment with accurate tools to achieve the precise measurement required to deliver a desired result. While methane detection tools in the highly variable field environment of oil and gas operations may not be able to provide precise measurement and accounting for all emissions, they can still provide actionable data that drives reduction in emissions and climate benefit.
The quantification provided by these tools is sufficient to improve understanding of emissions and achieve significant emissions reduction. Even with an error range of +/- 70%, we can still distinguish between large emissions sources that require immediate attention and smaller sources that may not be as urgent and use the data to implement preventative measures and engineered solutions to eliminate future occurrences.
Ultimately, it is the resulting reduction in emissions that matter, not the precision of the measurement.
Singh, D., Barlow, B., Hugenholtz, C., Funk, W., Robinson, C., and Ravikumar, A; Field Performance of New Methane Detection Technologies: Results from the Alberta Methane Field Challenge; Center for Environment; Energy, and Economy, Harrisburg University of Science and Technology, Harrisburg, PA
 : Heltzel, R.; Johnson, D.; Zaki, M.; Gebreslase, A.; Abdul-Aziz, O.I. Understanding the Accuracy Limitations of Quantifying Methane Emissions Using Other Test Method 33A. Environments 2022, 9, 47. https://doi.org/10.3390/ environments9040047
 Halley L. Brantley, Eben D. Thoma, William C. Squier, Birnur B. Guven, and David Lyon Assessment of Methane from Oil and Gas Production Pads using Mobile Measurement, Environmental Science & Technology 2014 48 (24), 14508-14515