可伸缩式PDC-孕镶金刚石耦合仿生智能钻头的破岩仿真

Rock breaking simulation of flexible PDC-impregnated diamond coupling bionic intelligent bit

吴泽兵 席凯凯 王杰 李超 程欢 杨晨娟

WU Zebing XI Kaikai WANG Jie LI Chao CHENG Huan YANG Chenjuan

西安石油大学机械工程学院

Mechanical Engineering College, Xi’an Shiyou University, Xi’an 710065, Shaanxi, China

针对钻井过程中岩性识别困难、PDC钻头磨损严重、孕镶金刚石钻头破岩效率低和钻头泥包等问题，采用BP神经网络学习算法建立了岩性识别模型，并设计了一种新型可伸缩式PDC-孕镶金刚石耦合仿生智能钻头，分别在软件Matlab和ABAQUS上进行了岩性识别仿真和破岩仿真。仿真结果表明：BP神经网络模型对岩性的识别精度非常高，有利于合理选择钻头类型、及时调整钻井参数和提高钻井效率；仿生智能钻头作用于岩石表面的应力远大于常规钻头，使岩石更易达到破碎极限，从而提高钻头钻速。本设计集智能岩性识别、高破岩效率、自再生功能、防泥包功能于一体，为我国石油钻头的设计提供了一种新思路，具有重要意义。

To cope with difficult lithology identification, serious wear of PDC bit, low rock breaking efficiency of impregnated diamond bit and bit balling in the process of drilling, this paper established the lithology identification model by means of BP neural network leaning algorithm. In addition, a novel flexible PDC-impregnated diamond coupling bionic intelligent bit was designed and then simulated in Matlab and ABAQUS, respectively. The simulation results show that the BP neural network model can identify lithology very accurately, which is beneficial to select bit type reasonably, adjust drilling parameters in time and improve drilling efficiency. Compared with conventional bits, the stress applied on rock surface by the bionic intelligent bit is much higher, so it can reach the rock breaking limit more easily and its drilling speed is improved. This design integrates intelligent lithology identification, high rock breaking efficiency, self-regeneration function and anti-balling function and provides a new idea for the design of petroleum bits in China.

10.13639/j.odpt.2021.04.010