From deviation risk to trajectory complexity: machine learning-based deviation characterization and tortuosity analysis for wellbore quality assessment.
- DOI
- 10.1038/s41598-026-48924-2
- Published
- 2026 May 4
- Container
- Scientific reports
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1038/s41598-026-48924-2,
title = {From deviation risk to trajectory complexity: machine learning-based deviation characterization and tortuosity analysis for wellbore quality assessment.},
author = {Bhattacherjee R and Ahmed N and Sutradhor SS},
year = {2026},
journal = {Scientific reports},
doi = {10.1038/s41598-026-48924-2},
url = {https://doi.org/10.1038/s41598-026-48924-2}
}RIS
TY - JOUR TI - From deviation risk to trajectory complexity: machine learning-based deviation characterization and tortuosity analysis for wellbore quality assessment. AU - Bhattacherjee R AU - Ahmed N AU - Sutradhor SS PY - 2026 JO - Scientific reports DO - 10.1038/s41598-026-48924-2 UR - https://doi.org/10.1038/s41598-026-48924-2 ER -
APA
R, B., N, A., & SS, S. (2026). From deviation risk to trajectory complexity: machine learning-based deviation characterization and tortuosity analysis for wellbore quality assessment.. Scientific reports. https://doi.org/10.1038/s41598-026-48924-2
Source records
- pubmed · retrieved 2026-09-26T05:44:38.798Z