LIANG Dong-cai, TANG Hua, WU Zhen-jun, et al. Stratum identification based on multiple drilling parameters and
probability classification[J]. 2022, 43(4): 1123-1134.
DOI:
LIANG Dong-cai, TANG Hua, WU Zhen-jun, et al. Stratum identification based on multiple drilling parameters and
probability classification[J]. 2022, 43(4): 1123-1134.DOI: 10.16285/j.rsm.2021.0528.
Stratum identification based on multiple drilling parameters and
probability classification
The traditional 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. The cooperative effect of multiple parameters in drilling process should be considered. Firstly
the advanced drilling data was preprocessed
including standardization
frequency distribution analysis and sensitivity analysis
to select the key drilling parameters 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 indexes of rock breaking energy
logistic regression probability and stratum hardness index were established respectively. Finally
a stratum identification model was established by probability classification method based on Bayesian principle
and the model parameters were obtained 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 indexes have great performance in cross-hole stratum identification
and the identification accuracy exceeds 80%. The rock breaking energy and the logic 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 logical 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%.