Intelligent identification method of formation structure while drilling based on PCA-PSO-RF

Abstract:

To address the problems of low efficiency and information lag in conventional core drilling， as well as the limited classification accuracy of existing while-drilling identification models caused by the neglect of parameter multicollinearity and hyperparameter optimization， an intelligent while-drilling identification method for formation structure is proposed based on the fusion of principal component analysis （PCA）， particle swarm optimization （PSO）， and random forest （RF）. First， four features-feed pressure， rotational speed， torque， and pump pressure-that are highly correlated with formation structure are selected from multiple while-drilling parameters using Pearson correlation analysis. Second， PCA is applied to reduce the original four-dimensional features to three principal components with a cumulative contribution rate exceeding 95%， thereby eliminating multicollinearity among the features. Finally， the PSO algorithm is used to globally optimize the hyperparameters of the RF classifier. The model is constructed based on measured data and compared with various other algorithms. The results indicate that the PCA-PSO-RF fusion model achieves an F1-score of 0.964， with both precision and recall exceeding 96%， significantly outperforming the unoptimized RF model and mainstream gradient boosting algorithms. The validation accuracy on an independent borehole dataset is 84.8%， and the prediction accuracy for unlabeled data is 83%， with a misclassification rate of only 2% for intact rock sections， demonstrating the model''s robustness and generalization capability. This study achieves real-time， high-precision identification of fractured and intact rock sections， providing reliable technical support for intelligent drilling and geotechnical engineering investigation.