Prediction of penetration rate of rotary-percussive drilling using artificial neural networks – a case study / Prognozowanie postępu wiercenia przy użyciu wiertła udarowo-obrotowego przy wykorzystaniu sztucznych sieci neuronowych – studium przypadku

Abstract

Penetration rate in rocks is one of the most important parameters of determination of drilling economics. Total drilling costs can be determined by predicting the penetration rate and utilized for mine planning. The factors which affect penetration rate are exceedingly numerous and certainly are not completely understood. For the prediction of penetration rate in rotary-percussive drilling, four types of rocks in Sangan mine have been chosen. Sangan is situated in Khorasan-Razavi province in Northeastern Iran. The selected parameters affect penetration rate is divided in three categories: rock properties, drilling condition and drilling pattern. The rock properties are: density, rock quality designation (RQD), uni-axial compressive strength, Brazilian tensile strength, porosity, Mohs hardness, Young modulus, P-wave velocity. Drilling condition parameters are: percussion, rotation, feed (thrust load) and flushing pressure; and parameters for drilling pattern are: blasthole diameter and length. Rock properties were determined in the laboratory, and drilling condition and drilling pattern were determined in the field. For create a correlation between penetration rate and rock properties, drilling condition and drilling pattern, artificial neural networks (ANN) were used. For this purpose, 102 blastholes were observed and drilling condition, drilling pattern and time of drilling in each blasthole were recorded. To obtain a correlation between this data and prediction of penetration rate, MATLAB software was used. To train the pattern of ANN, 77 data has been used and 25 of them found for testing the pattern. Performance of ANN models was assessed through the root mean square error (RMSE) and correlation coefficient (R 2 ). For optimized model (14-14-10-1) RMSE and R 2 is 0.1865 and 86%, respectively, and its sensitivity analysis showed that there is a strong correlation between penetration rate and RQD, rotation and blasthole diameter. High correlation coefficient and low root mean square error of these models showed that the ANN is a suitable tool for penetration rate prediction.Keywords: Penetration rate, Rotary-percussive drilling, Artificial neural networks, Top hammer drilling, Sangan iron mine Postęp wiercenia przy wierceniach skał jest jednym z podstawowych parametrów decydujących o opłacalności przedsięwzięcia. Całkowite koszty prowadzenia prac wiertniczych określa się w oparciu

Study on Evaluation and Prediction for Shale Gas PDC Bit in Luzhou Block Sichuan Based on BP Neural Network and Bit Structure

“…It is hard to obtain the geological conditions and coefficients of drilling parameters, and it is difficult to effectively represent the ROP as a mathematical function of some critical variables. As a result, some studies in recent years have recommended using a data-driven model in the field of artificial intelligence to forecast and optimize the ROP, and the neural network method has been widely adopted [17,18]. Su Xinghua [19] and Omogbolahan S Ahmed et al [20] used integrated algorithms to build ROP prediction models based on K nearest neighbor and decision tree methods, with goodness of fit serving as the ROP prediction evaluation metric.…”

Artificial neural networks models for rate of penetration prediction in rock drilling

“…Aalizad and Rashidinejad [1] studied the predictability of the ROP of rotary-percussive drilling based on intact rock properties, rock mass characteristics, the operational drilling parameters and some blast-hole parameters. Another recent work by Kahraman [2] investigated the ROP of percussive drills based on indirect tests.…”