,
pages 1-22
Research on the application of particle swarm optimization algorithm based on heterogeneous lithology analysis and drilling parameter correction to optimize the back propagation neural network rate of penetration prediction model
2
Jidong Oilfield Company, Petrochina Company Limited
3
Daqing Drilling Engineering Company, CNPC
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Publication type: Journal Article
Publication date: 2025-03-13
scimago Q3
wos Q2
SJR: 0.404
CiteScore: 2.9
Impact factor: 1.4
ISSN: 10916466, 15322459
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Fang X. et al. Research on the application of particle swarm optimization algorithm based on heterogeneous lithology analysis and drilling parameter correction to optimize the back propagation neural network rate of penetration prediction model // Petroleum Science and Technology. 2025. pp. 1-22.
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Fang X., Hu Z., Song W., Zhou Y., Tan Y. Research on the application of particle swarm optimization algorithm based on heterogeneous lithology analysis and drilling parameter correction to optimize the back propagation neural network rate of penetration prediction model // Petroleum Science and Technology. 2025. pp. 1-22.
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TY - JOUR
DO - 10.1080/10916466.2025.2477657
UR - https://www.tandfonline.com/doi/full/10.1080/10916466.2025.2477657
TI - Research on the application of particle swarm optimization algorithm based on heterogeneous lithology analysis and drilling parameter correction to optimize the back propagation neural network rate of penetration prediction model
T2 - Petroleum Science and Technology
AU - Fang, Xinjie
AU - Hu, Zhongzhi
AU - Song, Wei
AU - Zhou, Yan
AU - Tan, Yuelong
PY - 2025
DA - 2025/03/13
PB - Taylor & Francis
SP - 1-22
SN - 1091-6466
SN - 1532-2459
ER -
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@article{2025_Fang,
author = {Xinjie Fang and Zhongzhi Hu and Wei Song and Yan Zhou and Yuelong Tan},
title = {Research on the application of particle swarm optimization algorithm based on heterogeneous lithology analysis and drilling parameter correction to optimize the back propagation neural network rate of penetration prediction model},
journal = {Petroleum Science and Technology},
year = {2025},
publisher = {Taylor & Francis},
month = {mar},
url = {https://www.tandfonline.com/doi/full/10.1080/10916466.2025.2477657},
pages = {1--22},
doi = {10.1080/10916466.2025.2477657}
}