Regressive and Big-Data-Based Analyses of Rock Drillability Based on Drilling Process Monitoring (DPM) Parameters

Abstract

Accurate, rapid and effective analysis of rock drillability is very important for mining, civil and petroleum engineering. In this study, a method of rock drillability evaluation based on drilling process monitoring (DPM) parameters is proposed by using the field drilling test data. The revolutions per minute (N), thrust, torque and rate of penetration (ROP) were recorded in real time. Then, the two-dimensional regression analysis was utilized to investigate the relationships between the drilling parameters, and the three-dimensional regression analysis was used to establish models of ROP and specific energy (SE), in which the N-F-ROP, N-T-ROP and the improved SE model were obtained. In addition, the random forest (RF) and support vector machine combined with genetic algorithm (GA-SVM) were applied to predict rock drillability. Finally, a prediction model of uniaxial compressive strength (UCS) was established based on the SE and drillability index, Id. The results show that both regression models and prediction models have good performance, which can provide important guidance and a data source for field drilling and excavation processes.