Abstract

Abstract. Advances in natural gas extraction technology have led to increased activity in the production and transport sectors in the United States and, as a consequence, an increased need for reliable monitoring of methane leaks to the atmosphere. We present a statistical methodology in combination with an observing system for the detection and attribution of fugitive emissions of methane from distributed potential source location landscapes such as natural gas production sites. We measure long (> 500 m), integrated open-path concentrations of atmospheric methane using a dual frequency comb spectrometer and combine measurements with an atmospheric transport model to infer leak locations and strengths using a novel statistical method, the non-zero minimum bootstrap (NZMB). The new statistical method allows us to determine whether the empirical distribution of possible source strengths for a given location excludes zero. Using this information, we identify leaking source locations (i.e., natural gas wells) through rejection of the null hypothesis that the source is not leaking. The method is tested with a series of synthetic data inversions with varying measurement density and varying levels of model–data mismatch. It is also tested with field observations of (1) a non-leaking source location and (2) a source location where a controlled emission of 3.1 × 10−5 kg s−1 of methane gas is released over a period of several hours. This series of synthetic data tests and outdoor field observations using a controlled methane release demonstrates the viability of the approach for the detection and sizing of very small leaks of methane across large distances (4+ km2 in synthetic tests). The field tests demonstrate the ability to attribute small atmospheric enhancements of 17 ppb to the emitting source location against a background of combined atmospheric (e.g., background methane variability) and measurement uncertainty of 5 ppb (1σ), when measurements are averaged over 2 min. The results of the synthetic and field data testing show that the new observing system and statistical approach greatly decreases the incidence of false alarms (that is, wrongly identifying a well site to be leaking) compared with the same tests that do not use the NZMB approach and therefore offers increased leak detection and sizing capabilities.

Highlights

  • The combustion of natural gas in high-efficiency power cycles is cleaner and produces less climate-warming carbon dioxide gas than the combustion of coal (Environmental Protection Agency, 2015), which has led to interest in natural gas as a cleaner alternative to coal for energy generation

  • The results show that success in leak detection is much higher using non-zero minimum bootstrap (NZMB) compared with the non-bootstrap tests

  • None of the NZMB tests result in the occurrence of a falsepositive leak location, and only tests with low numbers of beams relative to the number of source locations fail to find both of the true leaks

Read more

Summary

Introduction

The combustion of natural gas in high-efficiency power cycles is cleaner and produces less climate-warming carbon dioxide gas than the combustion of coal (Environmental Protection Agency, 2015), which has led to interest in natural gas as a cleaner alternative to coal for energy generation. This configuration means that under most wind conditions (wind directions within ≈ 40◦ of orthogonal to the beam array in either direction), one beam is situated upwind and one beam is situated downwind of each source location With this method, we attempt to remove the timevarying CH4 concentration to which enhancements from discrete near-field emissions are added. After a linear interpolation to upwind measurements has been applied, this background is subtracted from measurements on the downwind beam to yield a measure of the CH4 enhancement due to fluxes at the source location Applying this method, the mean and standard deviation of the enhancement above background on retroreflector 1 – which is downwind of source location 1 (leak rate of 3.1 × 10−5 kg s−1) – is 17.4 ± 10.1 ppb. Precision could be improved by averaging data over a shorter time span (70 s), but those gains would be minimal (Fig. 9)

Objectives
Methods
Results
Discussion
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call