Environmental Studies and Forestry

Deep learning for detecting and characterizing oil and gas well pads in satellite imagery

N. Ramachandran, J. Irvin, et al.

This research, conducted by Neel Ramachandran, Jeremy Irvin, Mark Omara, Ritesh Gautam, Kelsey Meisenhelder, Erfan Rostami, Hao Sheng, Andrew Y. Ng, and Robert B. Jackson, presents a groundbreaking deep learning approach to mapping oil and gas infrastructure using high-resolution satellite imagery, revealing previously unmapped well pads and storage tanks in key basins.

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Index

Abstract

Methane emissions from the oil and gas sector significantly contribute to climate change. This research presents a deep learning approach using freely available, high-resolution satellite imagery to automatically map oil and gas well pads and storage tanks. The approach demonstrates high performance in the Permian and Denver-Julesburg basins, identifying a substantial number of previously unmapped well pads and storage tanks. While challenges remain, this method offers a scalable framework for globally mapping oil and gas infrastructure.

Publisher

Nature Communications

Published On

Aug 15, 2024

Authors

Neel Ramachandran, Jeremy Irvin, Mark Omara, Ritesh Gautam, Kelsey Meisenhelder, Erfan Rostami, Hao Sheng, Andrew Y. Ng, Robert B. Jackson

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