My research focuses on the prediction, optimization, and control of wellbore trajectories during drilling operations. I employ methodologies such as machine learning, drillstring mechanics, mechanism-data integration, and multi-objective optimization to address these challenges. I am particularly interested in combining advanced data-driven models with physics-based approaches to develop innovative solutions for intelligent drilling systems.

Integrating Mechanics and Machine Learning for Build-up rate Prediction

Li, Z., Song, X., Yu, Q., Gong, N., Jiang, Z., Zhu, Z., & Zhang, C.

Geoenergy Science and Engineering, 213594, (2024)

Real-Time Prediction of Wellbore Trajectory with a Dual-Input GRU (Di-GRU) Model.

Zhen, L., Xianzhi, S., Zheng, W., Zhenxin, J., Tao, P., & Zhaopeng, Z.

Offshore Technology Conference Asia, (2024), 45(4): 393-403

Intelligent prediction of well trajectory based on dual-input sequence-to-sequence model

LI Zhen, SONG Xianzhi, LI Gensheng, ZHANG Hongning

Oil Drilling & Production Technology, (2023)

Predicting Rate of Penetration Using the Dual Seq2Seq Model

Pan T, Li Z, Zhang C

International Geomechanics Symposium, (2023)

•  PhD Candidate., Petroleum Engineering, China University of Petroleum (Beijing), Beijing, China, 2025
•  MS Candidate., Petroleum Engineering, China University of Petroleum (Beijing), Beijing, China, 2022
•  Bachelor's degree, Petroleum Engineering, China University of Petroleum (Beijing), Beijing, China, 2019

• Physical Science and Engineering Division (PSE)