Methane is a potent greenhouse gas, with a global warming potential approximately 30 times greater than carbon dioxide over a 100-year period, according to the UN Intergovernmental Panel on Climate Change (IPCC). At COP26, countries worldwide committed to the Global Methane Pledge (GMP), aiming to reduce methane emissions by 30% by 2030. The oil and gas sector is responsible for about one-quarter of all methane emissions from human activities. A Bloomberg report highlights that approximately 80% of methane emissions from this sector arise from upstream production, including methane fugitive emissions and activities such as venting and flaring. 

North America faces stringent regulations and steep fines regarding methane, and publicly traded oil and gas companies are also under increasing pressure from investors to reduce methane emissions, or “demethanize” their operations. Companies here are leading the investment and adoption of advanced methane detection systems, particularly in key oil-producing regions such as the Permian, Anadarko, and Appalachian basins in the U.S.

As an upstream oil and gas company, the client dedicates significant resources to monitoring and reducing methane emissions to meet regulatory targets and industry standards. A specialized team within their HSE and carbon portfolio is focused on managing methane emissions to support the company’s net-zero and OMGP 2.0 goals, while ensuring alignment with EPA LDAR and the Super Emitter Program requirements. This called for a robust methane data management system enabled the timely capture, accurate tracking, and effective reporting of emissions.

LTIMindtree worked with the HSE and carbon team to build the end-to-end architecture and workflow for a methane emissions management system. This included the automation of data transfer from multiple sources of data for methane such as drones, sensors, manned and unmanned aircrafts and public satellite detections. The solution’s robust data pipeline and data transfer and consolidation logic ensured correct attribution of detections to specific equipment and facilities. Security considerations through virus scans for external data, and masking policies to adhere to regulations around midstream data visibility were rigorously maintained. The legacy system data transfers took place through the SFTP path or via API transfers. We introduced a Snowflake-to-Snowflake transfer for tabular data with a vendor for speed of transfer and heightened data security measures.