Assessing the Application of Drone TDLAS Methane Emissions Monitoring Technology in the Intertropical Convergence Zone Using Machine

Abstract Global energy stakeholders are increasingly becoming more committed to global methane reduction and emissions transparency. These organizations have global reach and production processes which can pose unique problems for consistent measurement and verification techniques. To help provide more consistent measurements across the globe, this study evaluates the efficacy of a drone-mounted TDLAS sensor for use in the Inter-tropical Convergence Zone (ITCZ), an area of the world plagued with dampened windspeeds often less than 2 m s-1. This environment makes accurate measurements of point source emission rates challenging for advanced emissions monitoring technologies which is a substantial roadblock in the implementation of OGMP 2.0 best practices for Level 5 emissions monitoring. We simulated errors in mass-balance derived methane emission rates by utilizing a Gaussian plume model and drone flight paths with a vertical raster pattern at a 10Hz sensor sampling frequency. The Gaussian plume model allows for simple theoretical equations as a function of plume rise, downwind distance from the source, plume dispersion, and altitude-dependent wind velocity to be explicitly accounted for to understand sensitivity from errors in each of these terms. We conducted a Monte Carlo simulation and explored uncertainty across all sources. Finally, we built a machine learning (ML) random forest (RF) classifier to predict survey success based on prevailing conditions and survey design parameters to offer a comprehensive approach to assessing and mitigating uncertainties in methane emission measurements. We find that survey settings need to be carefully considered along with plume effects to provide accurate measurements in the field. To illustrate, we show a case study with two flights, both surveying flares but with different flight settings, to achieve the desired error < 30%. Our case study showed that mid-range wind speeds can achieve high survey success with lower resolution surveys (i.e., faster flight velocity and larger vertical step) whereas low-range wind speeds require higher resolution for best results (i.e., lower flight velocity and lower vertical step).