Research on Stick-slip Vibration Level Estimation of Near-bit Based on Optimized XGBoost

Authors

Hanwen Tang, 1. Beijing Key Laboratory of High Dynamic Navigation Technology, Beijing Information Science & Technology University, Beijing 100101, China;;
Zhang Tao, 1. Beijing Key Laboratory of High Dynamic Navigation Technology, Beijing Information Science & Technology University, Beijing 100101, China;;
Yumei Li, 1. Beijing Key Laboratory of High Dynamic Navigation Technology, Beijing Information Science & Technology University, Beijing 100101, China;;
Li Lei, 2. Drillling & Production Technology Research Institute, CNPC Chuanqing Exploratory Drilling Engineering Co., Ltd., Deyang 618300, China;;
Jinghua Zhang, 3. Engineering Technology Research Institute, CNPC Bohai Drilling Engineerinig Co., Ltd., Tianjin 300457, China;;
Dongliang Hu, 4. Directional Well Drilling Company, CNPC Bohai Drilling Engineerinig Co., Ltd., Tianjin 300000, China;

Abstract

Abstract: Stick-slip vibration is an important limiting factor affecting drilling speed, safety and cost. The establishment of a reliable stick-slip vibration classification model is very important for oil drilling decision-making. A new method based on Bayesian optimization and eXtreme Gradient Boosting (XGBoost) is proposed to evaluate the severity of stick-slip vibration near the bit. The classification processing of the near-bit stick-slip vibration data is carried out. The main feature vectors of the original data is extracted through time domain and frequency domain analysis. A stick-slip vibration level identification and prediction model based on XGBoost is established, and Bayesian algorithm is uesd to the XGBoost hyperparameter tuning. Based on the model evaluation indicators of accuracy, recall, and F1 score, the test set is used to compare the optimized XGBoost model with the Classification and Regression Tree (CART) model and the Random Forests (RFs) model, respectively. The research results show that the method can effectively evaluate the stick-slip vibration level of the near-bit bit and has a high recognition accuracy.

Recommended Citation

Tang, Hanwen; Tao, Zhang; Li, Yumei; Lei, Li; Zhang, Jinghua; and Hu, Dongliang (2021) "Research on Stick-slip Vibration Level Estimation of Near-bit Based on Optimized XGBoost," Journal of System Simulation: Vol. 33: Iss. 11, Article 19.
DOI: 10.16182/j.issn1004731x.joss.21-FZ0722
Available at: https://dc-china-simulation.researchcommons.org/journal/vol33/iss11/19

First Page

2704

Revised Date

2021-07-20

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.21-FZ0722

Last Page

2710

CLC

TP29

Recommended Citation

Tang Hanwen, Zhang Tao, Li Yumei, Li Lei, Zhang Jinghua, Hu Dongliang. Research on Stick-slip Vibration Level Estimation of Near-bit Based on Optimized XGBoost[J]. Journal of System Simulation, 2021, 33(11): 2704-2710.

DOI

10.16182/j.issn1004731x.joss.21-FZ0722