Predictive Modeling of Drillability and Bit Wear Index for Efficient Drilling and Tunneling Operations

Drillability and tool wear are critical factors influencing the efficiency and cost-effectiveness of deep well drilling for oil and gas, as well as tunneling projects. Understanding these parameters is essential for optimizing advance rates, reducing operational costs, minimizing downtime, and improving spare parts management. The Drilling Rate Index (DRI), Bit Wear Index (BWI), and Cutter Life Index (CLI), developed by NTNU/SINTEF, serve as key indicators for evaluating drilling and Tunnel Boring Machine (TBM) performance. While extensive research has been conducted on the impact of rock properties on drillability, limited studies have focused on predicting bit wear using BWI. This study investigates the relationship between various geo-mechanical rock parameters and both drillability and bit wear. Experimental analyses reveal that rock properties such as uniaxial compressive strength, Young's modulus, porosity, and abrasivity significantly influence these indices. Furthermore, predictive models with high accuracy (R² ≥ 0.83) were developed using Simple Linear Regression Analysis (SLRA) and Multiple Linear Regression Analysis (MLRA) to estimate DRI and BWI. The findings provide valuable insights for optimizing drilling operations, enhancing tool selection, and improving cost estimation in mining, oil, and tunneling industries.