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

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

doi:10.1038/s41467-024-50334-9

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

Nat Commun. 2024 Aug 15;15(1):7036. doi: 10.1038/s41467-024-50334-9.

Authors

Neel Ramachandran^#^{1

2}, Jeremy Irvin^#³, Mark Omara⁴, Ritesh Gautam⁴, Kelsey Meisenhelder⁴, Erfan Rostami³, Hao Sheng³, Andrew Y Ng³, Robert B Jackson^{5

6}

Affiliations

¹ Stanford Research Computing, Stanford University, Stanford, CA, USA. neelr@stanford.edu.
² Department of Earth System Science, Stanford University, Stanford, CA, USA. neelr@stanford.edu.
³ Department of Computer Science, Stanford University, Stanford, CA, USA.
⁴ Environmental Defense Fund, Austin, TX, USA.
⁵ Department of Earth System Science, Stanford University, Stanford, CA, USA.
⁶ Woods Institute for the Environment and Precourt Institute for Energy, Stanford University, Stanford, CA, USA.

^# Contributed equally.

Abstract

Methane emissions from the oil and gas sector are a large contributor to climate change. Robust emission quantification and source attribution are needed for mitigating methane emissions, requiring a transparent, comprehensive, and accurate geospatial database of oil and gas infrastructure. Realizing such a database is hindered by data gaps nationally and globally. To fill these gaps, we present a deep learning approach on freely available, high-resolution satellite imagery for automatically mapping well pads and storage tanks. We validate the results in the Permian and Denver-Julesburg basins, two high-producing basins in the United States. Our approach achieves high performance on expert-curated datasets of well pads (Precision = 0.955, Recall = 0.904) and storage tanks (Precision = 0.962, Recall = 0.968). When deployed across the entire basins, the approach captures a majority of well pads in existing datasets (79.5%) and detects a substantial number (>70,000) of well pads not present in those datasets. Furthermore, we detect storage tanks (>169,000) on well pads, which were not mapped in existing datasets. We identify remaining challenges with the approach, which, when solved, should enable a globally scalable and public framework for mapping well pads, storage tanks, and other oil and gas infrastructure.