Evaluating Machine Learning Approaches to Identify and Predict Oil and Gas Produced Water Lithium Concentrations

Emil Attanasi, Bonnie McDevitt, Philip Freeman, Timothy Coburn

Open source

DOI: 10.1080/26941899.2026.2624195
Published: 12
Container: Data Science in Science
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1080/26941899.2026.2624195,
  title = {Evaluating Machine Learning Approaches to Identify and Predict Oil and Gas Produced Water Lithium Concentrations},
  author = {Emil Attanasi and Bonnie McDevitt and Philip Freeman and Timothy Coburn},
  year = {2026},
  journal = {Data Science in Science},
  doi = {10.1080/26941899.2026.2624195},
  url = {https://doi.org/10.1080/26941899.2026.2624195}
}

RIS

TY  - JOUR
TI  - Evaluating Machine Learning Approaches to Identify and Predict Oil and Gas Produced Water Lithium Concentrations
AU  - Emil Attanasi
AU  - Bonnie McDevitt
AU  - Philip Freeman
AU  - Timothy Coburn
PY  - 2026
JO  - Data Science in Science
DO  - 10.1080/26941899.2026.2624195
UR  - https://doi.org/10.1080/26941899.2026.2624195
ER  -

APA

Attanasi, E., McDevitt, B., Freeman, P., & Coburn, T. (2026). Evaluating Machine Learning Approaches to Identify and Predict Oil and Gas Produced Water Lithium Concentrations. Data Science in Science. https://doi.org/10.1080/26941899.2026.2624195

Source records

doaj · retrieved 2026-09-26T13:00:39.488Z