关键词: 神经网络, 钻速, 冲击回转钻进, 风动潜孔锤

Abstract:

Percussive drilling has an incomparably priority to traditional rotary drilling methods in the aspect of penetration efficiency and the previous prediction of the drilling rate is only refered to rotary drilling. Practically, accurate prediction of the rate of penetration (ROP) of percussive drilling is very important in that it can help make the planning of the rock excavation projects in high efficiency. The multilayer neural network with back propagation algorithm (BPNN) has been employed to analyze the influence of the different parameters (i.e. thrust, rotational speed, pressure of compressed air, volume of compressed air, drilling depth and drilling bit operation time etc.) to ROP and a method of predicting ROP for the hollow-through type DTH drilling is also studied and applicated in the field drilling work with a very good agreement to actual values in the fields. At the same time, drilling parameters have been optimized to guide the penetration operation based on the prediction results.

Key words: neural network, the rate of penetration(ROP), percussive rotary drilling, DTH hammer