Addressing low-cost methane sensor calibration shortcomings with machine learning

Abstract

Quantifying methane emissions is essential for meeting near-term climate goals and is typically done using methane concentrations measured downwind of the source. One major source of methane important to observe and remediate is fugitive emissions from oil and gas productions sites; however, installing methane sensors at thousands of sites within a production basin can be prohibitively expensive. In recent years, relatively inexpensive metal oxide sensors have been used to measure methane concentrations at production sites. Current methods used to calibrate metal oxide sensors have been shown to have significant shortcomings, resulting in limited confidence in methane concentrations generated by these sensors. To address this, we investigate using a machine learning (ML) model to convert metal oxide sensor output to methane mixing ratios. To generate data to train this model, two metal oxide sensors, TGS2600 and TGS2611, were collocated with a trace methane analyzer downwind of controlled methane releases. A comparison of histograms generated using the analyzer and metal oxide sensors mixing ratios show overlap coefficients of 0.95 and 0.94 for the TGS2600 and TGS2611, respectively. Overall, our results showed there was good agreement between the ML derived metal oxide sensors' mixing ratios and those generated using the more accurate trace gas analyzer. This suggests that the response of lower-cost sensors calibrated using ML could be used to generate mixing ratios with higher precision and accuracy, thereby reducing the cost of sensor deployments, and allowing for timely and accurate tracking of methane emissions.