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

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.