Bayesian updating with machine learning and geostatistics for posterior predictions of unconventional reservoir performance
- DOI
- 10.1016/j.acags.2026.100408
- Published
- 12
- Container
- Applied Computing and Geosciences
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1016/j.acags.2026.100408,
title = {Bayesian updating with machine learning and geostatistics for posterior predictions of unconventional reservoir performance},
author = {D. Wang and J.T. Foster and Y. Lu and A. Thompson and M.J. Pyrcz},
year = {2026},
journal = {Applied Computing and Geosciences},
doi = {10.1016/j.acags.2026.100408},
url = {https://doi.org/10.1016/j.acags.2026.100408}
}RIS
TY - JOUR TI - Bayesian updating with machine learning and geostatistics for posterior predictions of unconventional reservoir performance AU - D. Wang AU - J.T. Foster AU - Y. Lu AU - A. Thompson AU - M.J. Pyrcz PY - 2026 JO - Applied Computing and Geosciences DO - 10.1016/j.acags.2026.100408 UR - https://doi.org/10.1016/j.acags.2026.100408 ER -
APA
Wang, D., Foster, J., Lu, Y., Thompson, A., & Pyrcz, M. (2026). Bayesian updating with machine learning and geostatistics for posterior predictions of unconventional reservoir performance. Applied Computing and Geosciences. https://doi.org/10.1016/j.acags.2026.100408
Source records
- doaj · retrieved 2026-11-10T11:40:38.478Z