Abstract:
Machine learning has emerged as the predominant approach for intelligent prediction of rock strength while drilling.To address the limitations of conventional methods characterized by homogeneous feature sets and compromised model generalizability,this study introduced a novel intelligent prediction methodology of rock strength while drilling based on heterogeneous model integration and Bayesian optimization.Firstly,the rock strength index system combining drilling parameters and mineral parameters was proposed,effectively overcoming the inherent constraints of traditional homogeneous feature dependency.Secondly,based on the Stacking heterogeneous integration framework,the four basic learners of Polynomial Regression (PR),Support Vector Machine (SVM),BP Neural Network (BPNN) and Random Forest (RF) were integrated,and Bayesian optimization (BO) was used to adaptively optimize the hyperparameters to establish a high-performance rock strength prediction model while drilling.Finally,an intelligent prediction method for rock strength while drilling was proposed.Based on the data set formed by the laboratory digital drilling test,a variety of intelligent prediction models of rock strength while drilling was established,and the reliability of the prediction method proposed in this study was verified.The results showed that the intelligent prediction model of rock strength while drilling based on heterogeneous model integration and Bayesian optimization is optimal in both the learning ability on the training set (
R2=0.9774,RMSE=5.89,MAPE=10.49%) and the generalization ability on the test set (
R2=0.9511,RMSE=8.45,MAPE=12.96%),which can realize the accurate and effective prediction of rock strength.Interpretability analysis showed that quartz content contributes the most to the predicted results,followed by feldspar content,quantitatively validating the critical importance of incorporating mineralogical parameters.