Abstract:

The conventional geological prediction method of advanced drilling usually takes the change rate of one specific drilling parameter as the main basis for stratum identification. The rock breaking of drill bit is a complicated mechanical process. Stratum identification with single drilling parameter results in great uncertainty. Thus the combined effect of multiple parameters in drilling process should be considered. Firstly, the advanced drilling data were preprocessed by SPSS, including standardization, frequency distribution analysis and sensitivity analysis, to select the key drilling parameters that are sensitive to stratum changes. Secondly, based on the principles of energy conservation, binary disordered logistic regression analysis and multi-parameter variability analysis, three comprehensive identification indices including rock breaking energy, logistic regression probability and stratum hardness were established respectively. Finally, the stratum identification model was established by probability classification method based on Bayesian principle, the model parameters were determined by ROC analysis method, and the stratum identification based on multiple drilling parameters and probability classification method was realized. Taking the tunnel project with complex geological conditions as an example, the application of the proposed stratum identification method is introduced. The results show that three comprehensive indices perform well in cross-hole stratum identification, and the identification accuracy exceeds 80%. The rock breaking energy and the logistic regression probability are suitable for the cross-hole stratum identification with short distance, and the average identification accuracies are 86.3% and 84.1%, respectively. The logistic regression probability index has strong identification capability for the weak interlayer, and the identification accuracy reaches 94.2%. The stratum hardness index is suitable for the cross-hole stratum identification with long distance, and the maximum identification accuracy of limestone is 93.2%.

Key words: advanced drilling, rock breaking energy, logistic regression probability, stratum hardness index, probability classification, stratum identification