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

Shaofeng Wang, Yu Tang, Ruilang Cao, Zilong Zhou and Xin Cai
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Shaofeng Wang: School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Yu Tang: School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Ruilang Cao: State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100048, China
Zilong Zhou: School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Xin Cai: School of Resources and Safety Engineering, Central South University, Changsha 410083, China

Mathematics, 2022, vol. 10, issue 4, 1-19

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, I d . 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.

Keywords: rock drillability; DPM parameters; regression analysis; RF; GA - SVM; UCS prediction model (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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