Exploitation
Babatunde Adebayo; Blessing Olamide Taiwo; BUSUYI THOMAS AFENI; Aderoju Oluwadolapo Raymond; Joshua Oluwaseyi Faluyi
Abstract
The quarry operators and managers are having a running battle in determining with precision the rate of deterioration of the button of the drill bit as well as its consumption. Therefore, this study is set to find the best-performing model for predicting the drill bit button's wear rate during rock drilling. ...
Read More
The quarry operators and managers are having a running battle in determining with precision the rate of deterioration of the button of the drill bit as well as its consumption. Therefore, this study is set to find the best-performing model for predicting the drill bit button's wear rate during rock drilling. Also, the rate at which drill bit buttons wear out during rock drilling in Ile-Ife, Osogbo, Osun State, and Ibadan, Oyo State, Southwest, Nigeria was investigated. Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and adaptive moment Estimation-based Long Short-Term Memory (LSTM) machine learning approaches were used to create models for estimating the bit wear rate based on circularity factor, rock grain size, equivalent quartz content, uniaxial compressive strength, porosity, and abrasive properties of the rock. The performance of the models was measured using a new error estimation index and four other convectional performance estimators. The analysis of performance shows that the adaptive moment estimation algorithm-based LSTM model did better and more accurately than the other models. Thus, the LSTM models presented can be used to improve drilling operations in real-life situations.
M. Capik; B. Batmunkh
Abstract
Modelling wear of drill bits can increase the efficiency in the drilling operations. Related to the subject, it is aimed to investigate the wear mechanism of drill bits. Wear in drill bits is influenced by many factors related to the drilling and rock properties. The type and intensity of wear are dependent ...
Read More
Modelling wear of drill bits can increase the efficiency in the drilling operations. Related to the subject, it is aimed to investigate the wear mechanism of drill bits. Wear in drill bits is influenced by many factors related to the drilling and rock properties. The type and intensity of wear are dependent on several complicated factors that are required to be considered in anticipating the rate of wear in the field and laboratory conditions. The laboratory tests have been performed in order to specify the relationships between the bit wear rate and the physico-mechanical properties, drillability, abrasive properties, and brittleness of rocks. Statistical analysis has been used to obtain equations for estimating the bit wear rate based on the rock properties. In this work, an ensemble technique is used to estimate the confidence interval and the prediction intervals for the regression models. This paper summaries the rock properties and bit wear mechanism, and argues the options to determine the bit wear rate. The test models indicate that the rock properties can give an idea of bit wear. They also show a good correlation between the bit wear rates. Also some models are developed to represent the wear quantification, and an approach is suggested in order to estimate the bit wear rate. The results obtained from studying the developed models provide a good agreement with the performance evaluation of an efficient drilling, which provide an indirect evaluation of drill bit wear rate during a drilling process, which can help to reduce the specific energy consumption and lower costs for the exchange of drill bits.
