Global Tracking and Quantification of Oil and Gas Methane Emissions from Recurrent Sentinel-2 Imagery

TL;DR

This study uses recurrent Sentinel-2 satellite imagery to detect and quantify over 800 methane emissions from global oil and gas infrastructure, revealing the prevalence of ultra-emitters and differences across countries.

Contribution

It introduces a method for monitoring and quantifying methane emissions globally using high-resolution satellite imagery and demonstrates the relationship between emissions detected by different sensors.

Findings

Detection of over 800 methane emission events globally

Establishment of a power law fit for emission estimates from 0.1 to 600 tCH4/hr

Identification of large differences in emissions across countries

Abstract

Methane (CH4) emissions estimates from top-down studies over oil and gas basins have revealed systematic under-estimation of CH4 emissions in current national inventories. Sparse but extremely large amounts of CH4 from oil and gas production activities have been detected across the globe, resulting in a significant increase of the overall O&G contribution. However, attribution to specific facilities remains a major challenge unless high-resolution images provide the sufficient granularity within O&G basin. In this paper, we monitor known oil-and-gas infrastructures across the globe using recurrent Sentinel-2 imagery to detect and quantify more than 800 CH4 emissions. In combination with emissions estimates from airborne and Sentinel-5P measurements, we demonstrate the robustness of the fit to a power law from 0.1 tCH4/hr to 600 tCH4/hr. We conclude here that the prevalence of ultra-emitters (> 25tCH4/hr) detected globally by Sentinel-5P directly relates to emission occurrences below its detection threshold. Similar power law coefficients arise from several major oil and gas producers but noticeable differences in emissions magnitudes suggest large differences in maintenance practices and infrastructures across countries.