Detecting Oil and Gas Land Dynamics in the Permian Basin by Using Deep Learning and Very High-resolution Aerial Images

The Permian Basin is one of the most significant oil and gas (O&G)-producing regions in the United States. The exploitation of O&G resources in this region has been going on for over a hundred years. Although the fossil energy output supports a large part of the country and contributes significantly to the national economy, there are several negative consequences associated with exploitation, including greenhouse gas emissions, biodiversity loss, ground instability and environmental pollution. Detecting O&G land and monitoring land change dynamics are crucial for mitigating these negative effects and managing resources effectively. In this research, we adopted a Convolutional Neural Network (CNN) based deep learning model to detect the O&G land in the Permian Basin from 2010 to 2018 using very high-resolution aerial images from the National Agriculture Imagery Program (NAIP). An O&G energy land change map is also produced from the classification results. Our prototype map of 2018 demonstrates that an accurate semantic segmentation algorithm over different land cover types at a 0.6 m spatial resolution is possible on super-computing clusters. Our results, anchored by a 2018 prototype, reveal significant changes in the extent and use of oil and gas (O&G) lands, demonstrating robustness in challenging environments such as bare ground, developed regions, and shrublands where the O&G land can be easily misclassified with the background objects. This sets the foundation for future time series analyses to further understand the dynamic nature of O&G activities across various regions. Our research offers the potential to generate an up-to-date map of O&G land change over large regions at a very high resolution, which can assist the government and stakeholders in making informed decisions and managing the resources accordingly.

Ma, Y., Song, X.P., Hernandez Serna, W.A., Li, H., Lu, Z. and Silva, J. (2024). Detecting Oil and Gas Land Dynamics in the Permian Basin by Using Deep Learning and Very High-resolution Aerial Images. AGU Fall Meeting, Abstract B33B-1546, December 9-13, Washington, D.C., USA.