A Deep Learning Based Framework to Identify Undocumented Orphaned Oil and Gas Wells from Historical Maps: A Case Study for California and Oklahoma

Journal Article · 04 December 2024 · Environmental Science and Technology

DOI: https://doi.org/10.1021/acs.est.4c04413 · OSTI ID:2479818

Ciulla, Fabio  ^[1]; Santos, Andre  ^[1]; Jordan, Preston ^[1]; Kneafsey, Timothy ^[1]; Biraud, Sebastien C. ^[1]; Varadharajan, Charuleka  ^[1]

---

1. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Undocumented Orphaned Wells (UOWs) are wells without an operator that have limited or no documentation with regulatory authorities. An estimated 310,000 to 800,000 UOWs exist in the United States (US), whose locations are largely unknown. These wells can potentially leak methane and other volatile organic compounds to the atmosphere, and contaminate groundwater. In this study, we developed a novel framework utilizing a state-of-the-art computer vision neural network model to identify the precise locations of potential UOWs. The U-Net model is trained to detect oil and gas well symbols in georeferenced historical topographic maps, and potential UOWs are identified as symbols that are further than 100 m from any documented well. A custom tool was developed to rapidly validate the potential UOW locations. We applied this framework to four counties in California and Oklahoma, leading to the discovery of 1301 potential UOWs across >40,000 km². We confirmed the presence of 29 UOWs from satellite images and 15 UOWs from magnetic surveys in the field with a spatial accuracy on the order of 10 m. This framework can be scaled to identify potential UOWs across the US since the historical maps are available for the entire nation.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: USDOE; USDOE Office of Fossil Energy and Carbon Management (FECM), Office of Resource Sustainability; USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF); USDOE Office of Science (SC), Biological and Environmental Research (BER). Earth & Environmental Systems Science (EESS)
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 2479818
• Journal Information: Environmental Science and Technology, Journal Name: Environmental Science and Technology Journal Issue: 50 Vol. 58; ISSN 0013-936X
• Publisher: American Chemical Society (ACS)Copyright Statement
• Country of Publication: United States
• Language: English