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

## TL;DR

This paper uses deep learning on satellite images to automatically map oil and gas infrastructure, improving methane emission tracking.

## Contribution

A novel deep learning framework for detecting oil and gas infrastructure in satellite imagery, achieving high accuracy and scalability.

## Key findings

- The method achieves high precision and recall for detecting well pads and storage tanks in satellite imagery.
- The approach detects over 70,000 new well pads and more than 169,000 storage tanks not previously mapped in existing datasets.

## 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.

This work uses deep learning on satellite imagery to map well pads and storage tanks in two major U.S. basins. The resulting data fills large gaps in existing databases, a crucial step for improving methane emission estimates and source attribution.