Penetration rate prediction for diamond bit drilling by adaptive neuro-fuzzy inference system and multiple regressions

Abstract

In many mining, civil, and petroleum engineering applications diamond bit drilling is widely used due to high penetration rate, core recovery and its ability to drill with less deviation. Recently, many research have been conducted to estimate the penetration rate of diamond drilling which can be considered as one of the most important parameters in project planning and cost estimation of the operation.A database covering the rock properties and the machine operational parameters collected from seven different drilling sites in Turkey is constructed. Construction of an adaptive neuro-fuzzy inference system and the multiple regression models for predicting the penetration rate of diamond drilling is described. In the models, rock properties such as the uniaxial compressive strength, the rock quality designation, and the equipment operational parameters like bit load and bit rotation are considered. Although the prediction performance of multiple regression models is high, the adaptive neuro-fuzzy inference model exhibits better performance based on the comparison of performance indicators. By using the models, penetration rate of diamond bit drilling can be predicted effectively.