Improving methane emission monitoring in Oman’s oil and gas sector with mobile measurements and well pad identification

Methane is a potent greenhouse gas and has become a global priority to combat global warming. In Oman, fugitive methane emissions from the oil and gas sector account for the majority (77%) of the country’s anthropogenic methane emissions. Oman’s primary oil and gas company has joined the Oil and Gas Methane Partnership 2.0 (OGMP 2.0), committing to monitoring and reducing their methane emissions. However, methane emissions from Oman’s oil and gas sector remain highly uncertain, and there have been no independent, academic-led ground-based measurement studies conducted in Oman until now.

To address this gap, the United Nations Environment Programme’s (UNEP) International Methane Emissions Observatory (IMEO) funded the first vehicle-based methane measurement campaign targeting oil and gas infrastructure in Oman in 2023 to improve data collection in measurement-scarce regions. Methane measurements were collected using vehicle-based Licor-7810 and Los Gatos MGGA-918 analysers, allowing high-resolution methane observations along accessible roads and offroad paths surrounding oil and gas infrastructure. Here, we present initial results across three oil and gas fields, including methane source attribution, detection, and quantification across various oil and gas infrastructure types.

In addition to methane detections, national methane emission estimates also depend on the number of oil and gas well pads and associated infrastructure that exist across the country, but this number remains highly uncertain. To improve estimates of oil and gas well counts, we introduce an initial framework for identifying oil and gas well pads from satellite imagery using machine learning.

By combining mobile measurement data and satellite imagery, we aim to improve methane monitoring in Oman’s largest anthropogenic methane-emitting sector, the oil and gas sector. This approach also demonstrates the value of relatively cost-effective vehicle-based screening methods for assessing emissions across large-scale oil and gas developments, and provides a foundation for similar efforts in regions with limited monitoring data.