Detecting Abandoned Oil Wells Using Machine Learning and Semantic Segmentation (Proposals Track) Spotlight

Michelle Lin (McGill University); David Rolnick (McGill University, Mila)

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Abstract

Around the world, there are millions of unplugged abandoned oil and gas wells, leaking methane into the atmosphere. The locations of many of these wells, as well as their greenhouse gas emissions impacts, are unknown. Machine learning methods in computer vision and remote sensing, such as semantic segmentation, have made it possible to quickly analyze large amounts of satellite imagery to detect salient information. This project aims to automatically identify abandoned oil wells in the province of Alberta, Canada to aid in estimating emissions and plugging high-emitting wells.