| Production performance of oil shale in-situ conversion with multilateral wells X Song, C Zhang, Y Shi, G Li Energy 189, 116145, 2019 | 61 | 2019 |
| U-net generative adversarial network for subsurface facies modeling C Zhang, X Song, L Azevedo Computational Geosciences 25 (1), 553-573, 2021 | 45 | 2021 |
| Real-time prediction of rate of penetration by combining attention-based gated recurrent unit network and fully connected neural networks C Zhang, X Song, Y Su, G Li Journal of Petroleum Science and Engineering 213, 110396, 2022 | 32 | 2022 |
| Real-time and multi-objective optimization of rate-of-penetration using machine learning methods C Zhang, X Song, Z Liu, B Ma, Z Lv, Y Su, G Li, Z Zhu Geoenergy Science and Engineering 223, 211568, 2023 | 18 | 2023 |
| Real-time prediction of logging parameters during the drilling process using an attention-based Seq2Seq model R Zhang, C Zhang, X Song, Z Li, Y Su, G Li, Z Zhu Geoenergy Science and Engineering 233, 212279, 2024 | 10 | 2024 |
| Bottom hole pressure prediction based on hybrid neural networks and Bayesian optimization C Zhang, R Zhang, Z Zhu, X Song, Y Su, G Li, L Han Petroleum Science 20 (6), 3712-3722, 2023 | 4 | 2023 |
| Early Gas Kick Warning Based on Temporal Autoencoder Z Zhu, D Zhou, D Yang, X Song, M Zhou, C Zhang, S Duan, L Zhu Energies 16 (12), 4606, 2023 | 2 | 2023 |
| Predicting Rate of Penetration Using the Dual Seq2Seq Model T Pan, Z Li, C Zhang, X Song, L Zhu, Z Yan, Z Zhu, Y Sun, P Ni ARMA/DGS/SEG International Geomechanics Symposium, ARMA-IGS-2023-0353, 2023 | 1 | 2023 |
| 水平井钻井提速-减阻-清屑多目标协同优化方法 丁建新， 李雪松， 宋先知， 张诚恺， 马宝东， 刘子豪， 祝兆鹏 石油机械 51 (11), 1-10, 2023 | 1 | 2023 |
| Intelligent Prediction of Rate of Penetration Using Mechanism-Data Fusion and Transfer Learning Z Huang, L Zhu, C Wang, C Zhang, Q Li, Y Jia, L Wang Processes 12 (10), 2133, 2024 | | 2024 |
| Intelligent Identification Workflow of Drilling Conditions Combining Deep Learning and Drilling Knowledge Z Liu, X Song, S Ye, B Ma, C Zhang, Z Wang, X Yao, Z Zhu, Y Wang International Conference on Offshore Mechanics and Arctic Engineering 87868 …, 2024 | | 2024 |
| Real-Time Prediction of Rate of Penetration Using Multi-Gene Genetic Programming B Ma, Z Zhu, X Song, C Zhang, Z Liu International Conference on Offshore Mechanics and Arctic Engineering 87868 …, 2024 | | 2024 |
| 钻柱摩阻扭矩智能预测模型与解释 刘慕臣， 宋先知， 李大钰， 朱硕， 付利， 祝兆鹏， 张诚恺， 潘涛 Coal Geology & Exploration 51 (9), 89-99, 2023 | | 2023 |
| A Novel Hybrid Transfer Learning Method for Bottom Hole Pressure Prediction R Zhang, X Song, G Li, Z Lv, Z Zhu, C Zhang, C Gong International Conference on Offshore Mechanics and Arctic Engineering 86915 …, 2023 | | 2023 |
| Integrating Mechanics and Machine Learning for Build-Up Rate Prediction Z Li, X Song, Q Yu, Z Jiang, Z Zhu, C Zhang Available at SSRN 4988832, 0 | | |
| Real-time prediction of oil and gas drilling rate based on physics-based model and particle filter method C Zhang, X Song, Y Su | | |
| Enhanced Prediction of Rate of Penetration Using the Interpretable Hybrid Temporal Graph Neural Network R Zhang, Z Zhu, X Song, G Li, Z Lv, C Zhang, C Wang Available at SSRN 4903909, 0 | | |
song xianzhichina university of petroleum-beijing在 cup.edu.cn 的电子邮件经过验证
song xianzhichina university of petroleum-beijing在 cup.edu.cn 的电子邮件经过验证