Surrogate-assisted hydraulic fracture optimization workflow with applications for shale gas reservoir development: a comparative study of machine learning models
- DOI
- 10.1016/j.ngib.2022.03.004
- Published
- 2022-06
- Container
- Natural Gas Industry B
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.ngib.2022.03.004,
title = {Surrogate-assisted hydraulic fracture optimization workflow with applications for shale gas reservoir development: a comparative study of machine learning models},
author = {Cong Xiao and Shicheng Zhang and Xinfang Ma and Tong Zhou and Xuechen Li},
year = {2022},
journal = {Natural Gas Industry B},
doi = {10.1016/j.ngib.2022.03.004},
url = {https://doi.org/10.1016/j.ngib.2022.03.004}
}RIS
TY - JOUR TI - Surrogate-assisted hydraulic fracture optimization workflow with applications for shale gas reservoir development: a comparative study of machine learning models AU - Cong Xiao AU - Shicheng Zhang AU - Xinfang Ma AU - Tong Zhou AU - Xuechen Li PY - 2022 JO - Natural Gas Industry B DO - 10.1016/j.ngib.2022.03.004 UR - https://doi.org/10.1016/j.ngib.2022.03.004 ER -
APA
Xiao, C., Zhang, S., Ma, X., Zhou, T., & Li, X. (2022). Surrogate-assisted hydraulic fracture optimization workflow with applications for shale gas reservoir development: a comparative study of machine learning models. Natural Gas Industry B. https://doi.org/10.1016/j.ngib.2022.03.004
Source records
- crossref · retrieved 2026-09-25T06:33:47.538Z