An intelligent prediction method and interpretability for drag and torque of drill string

Authors

LIU Muchen, College of Artificial Intelligence, China University of Petroleum(Beijing), Beijing 102249, ChinaFollow
SONG Xianzhi, College of Petroleum Engineering, China University of Petroleum(Beijing), Beijing 102249, China; National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum(Beijing), Beijing 102249, ChinaFollow
LI Dayu, College of Petroleum Engineering, China University of Petroleum(Beijing), Beijing 102249, China
ZHU Shuo, Jianghan Machinery Research Institute Limited Company of CNPC, Wuhan 430024, China
FU Li, CNPC Engineering Technology R&D Company Limited, Beijing 102206, China
ZHU Zhaopeng, National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum(Beijing), Beijing 102249, China; College of Mechaical and Transportation Engineering, China University of Petroleum(Beijing), Beijing 102249, China
ZHANG Chengkai, College of Petroleum Engineering, China University of Petroleum(Beijing), Beijing 102249, China
PAN Tao, College of Petroleum Engineering, China University of Petroleum(Beijing), Beijing 102249, China

Abstract

The accurate characterization and dynamic analysis of drilling string mechanics are essential to ensure the safe and efficient drilling. In the classical soft/rigid string model for drag & torque of drilling string, the friction coefficient of the drilling string is determined by empirical estimation or post-drilling inversion, of which the accuracy and timeliness needs to be improved. Based on the effectiveness of artificial intelligence technology applied in complex nonlinear mapping, a drag and torque prediction method of drill string with mechanism-data fusion was proposed by predicting the friction coefficient. Firstly, the friction coefficient was inversed using the drilled and logged well data and the soft-string model, which provides the data basis for intelligent prediction of friction coefficient. As a result of the data processing and quantitative feature analysis of 74 wells, a Long Short-Term Memory (LSTM) network considering data series features was established, and the reasonability of the model was verified through drag & torque prediction and the interpretability analysis by Shapley Additive explanation (SHAP). The results show that the prediction error of the friction coefficient is 5.89%, and the prediction error of drag & torque is reduced by 4.41%. The mapping relationship between the input features of the model characterization and the friction coefficient is consistent with the mechanical mechanism of drilling string, which indicates that the model has strong stability and interpretability. Generally, this method could provide the theoretical and technical support for accurate characterization and dynamic analysis of drilling string mechanics.

Keywords

intelligent prediction, drag & torque of drill string, friction coefficient, mechanism-data fusion, interpretability, deep learning

DOI

10.12363/issn.1001-1986.23.06.0330

Recommended Citation

LIU Muchen, SONG Xianzhi, LI Dayu, et al. (2023) "An intelligent prediction method and interpretability for drag and torque of drill string," Coal Geology & Exploration: Vol. 51: Iss. 9, Article 22.
DOI: 10.12363/issn.1001-1986.23.06.0330
Available at: https://cge.researchcommons.org/journal/vol51/iss9/22

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