Unsupervised Machine Learning framework for sensor placement optimization: analyzing methane leaks (Proposals Track)

Shirui Wang (University of Houston); Sara Malvar (Microsoft); Leonardo Nunes (Microsoft); Kim Whitehall (Microsoft); Yagna Oruganti (Microsoft); Yazeed Alaudah (Microsoft); Anirudh Badam (Microsoft)

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Abstract

Methane is one of the most potent greenhouse gases, with the global oil and gas industry being the second largest source of anthropogenic methane emissions, accounting for about 63% of the whole energy sector. This underscores the importance of detecting and remediating methane leaks for the entire oil and gas value chain. Methane sensor networks are a promising technology to detect methane leaks in a timely manner. While they provide near-real-time monitoring of an area of interest, the density of the network can be cost prohibitive, and the identification of the source of the leak is not apparent, especially where there could be more than one source. To address these issues, we developed a machine learning framework that leverages various data sources including oil and gas facilities data, historical methane leak rate distribution and meteorological data, to optimize sensor placement. The determination of sensor locations follows the objective to maximize the detection of possible methane leaks with a limited sensor budget.