A Deep Learning Based Framework to Identify Undocumented Orphaned Oil and Gas Wells from Historical Maps: A Case Study for California and Oklahoma
- DOI
- 10.1021/acs.est.4c04413
- Published
- 2024-12-04
- Container
- Environmental Science & Technology
- Publisher
- American Chemical Society (ACS)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1021/acs.est.4c04413,
title = {A Deep Learning Based Framework to Identify Undocumented Orphaned Oil and Gas Wells from Historical Maps: A Case Study for California and Oklahoma},
author = {Fabio Ciulla and Andre Santos and Preston Jordan and Timothy Kneafsey and Sebastien C. Biraud and Charuleka Varadharajan},
year = {2024},
journal = {Environmental Science \& Technology},
doi = {10.1021/acs.est.4c04413},
url = {https://doi.org/10.1021/acs.est.4c04413}
}RIS
TY - JOUR TI - A Deep Learning Based Framework to Identify Undocumented Orphaned Oil and Gas Wells from Historical Maps: A Case Study for California and Oklahoma AU - Fabio Ciulla AU - Andre Santos AU - Preston Jordan AU - Timothy Kneafsey AU - Sebastien C. Biraud AU - Charuleka Varadharajan PY - 2024 JO - Environmental Science & Technology DO - 10.1021/acs.est.4c04413 UR - https://doi.org/10.1021/acs.est.4c04413 ER -
APA
Ciulla, F., Santos, A., Jordan, P., Kneafsey, T., Biraud, S. C., & Varadharajan, C. (2024). A Deep Learning Based Framework to Identify Undocumented Orphaned Oil and Gas Wells from Historical Maps: A Case Study for California and Oklahoma. Environmental Science & Technology. https://doi.org/10.1021/acs.est.4c04413
Source records
- crossref · retrieved 2026-09-25T00:09:20.965Z