Machine learning and data-driven prediction of pore pressure from geophysical logs: A case study for the Mangahewa gas field, New Zealand
- DOI
- 10.1016/j.jrmge.2022.01.012
- Published
- 2022-12
- Container
- Journal of Rock Mechanics and Geotechnical Engineering
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.jrmge.2022.01.012,
title = {Machine learning and data-driven prediction of pore pressure from geophysical logs: A case study for the Mangahewa gas field, New Zealand},
author = {Ahmed E. Radwan and David A. Wood and Ahmed A. Radwan},
year = {2022},
journal = {Journal of Rock Mechanics and Geotechnical Engineering},
doi = {10.1016/j.jrmge.2022.01.012},
url = {https://doi.org/10.1016/j.jrmge.2022.01.012}
}RIS
TY - JOUR TI - Machine learning and data-driven prediction of pore pressure from geophysical logs: A case study for the Mangahewa gas field, New Zealand AU - Ahmed E. Radwan AU - David A. Wood AU - Ahmed A. Radwan PY - 2022 JO - Journal of Rock Mechanics and Geotechnical Engineering DO - 10.1016/j.jrmge.2022.01.012 UR - https://doi.org/10.1016/j.jrmge.2022.01.012 ER -
APA
Radwan, A. E., Wood, D. A., & Radwan, A. A. (2022). Machine learning and data-driven prediction of pore pressure from geophysical logs: A case study for the Mangahewa gas field, New Zealand. Journal of Rock Mechanics and Geotechnical Engineering. https://doi.org/10.1016/j.jrmge.2022.01.012
Source records
- crossref · retrieved 2026-09-25T10:40:39.017Z