基于BP神经网络的地质岩心钻探钻速预测研究  

Research on Prediction of Drilling Rate in Geological Core Drilling Based on BP Neural Network

作　　者：贾明让 胡远彪[1] 勾文超 周正 JIA Mingrang;HU Yuanbiao;GOU Wenchao;ZHOU Zheng(China University of Geosciences(Beijing),Beijing 100083,China)

机构地区：[1]中国地质大学(北京),北京100083

出　　处：《超硬材料工程》2024年第5期12-19,共8页Superhard Material Engineering

基　　金：国家重点研发计划战略性国际科技创新合作重点专项:“多金属矿岩心钻探关键技术装备联合研发及示范”(2016YFE0202200)。

摘　　要：地质岩心钻探目前在自动化、智能化方面的发展还并未成熟。钻探参数的选取、改进还主要通过经验来判断,并且需要提钻后通过对岩心的判断进行钻探参数的调整,具有一定的滞后性,降低了钻探的效率。因此,通过搭建地质岩心钻探试验台获取钻探数据,采取反向传播(Back-Propagation)算法,将钻压(WOB),扭矩(TOR),泵量(Q),回转速度(RPM)作为输入量,机械钻速(ROP)作为输出量,同时考虑钻头磨损和钻头切削深度对实验的影响。以每块不同混凝土块为单位,按照80/20划分训练集与测试集,通过数据处理后共得到6180组数据进行训练和测试,训练出最优的神经网络模型,可以对机械钻速(ROP)进行预测,预测精度可达94.1%,后续通过选取合适的钻进参数,可以实现地质岩心钻探速度的优化。本研究为地质岩心钻探的钻速预测,地质岩心钻机自动化提供参考。The development of automation and intelligence in geological core drilling is not yet mature.The selection and improvement of drilling parameters are mainly judged through experience,and adjustments to drilling parameters need to be made through the judgment of the core after drilling,which has a certain lag and reduces the efficiency of drilling.Therefore,by building a geological core drilling test bench to obtain drilling data,a Back Propagation algorithm is adopted,with WOB,TOR,Q,and RPM as input and ROP as output.At the same time,the impact of bit wear and bit cutting depth on the experiment is considered.Using each different concrete block as a unit,the training and testing sets are divided into 80/20.After data processing,a total of 6180 sets of data are obtained for training and testing,and the optimal neural network model is trained to predict the mechanical drilling rate(ROP)with a prediction accuracy of 94.1%.Subsequently,by selecting appropriate drilling parameters,the optimization of geological core drilling speed can be achieved.This study provides a reference for the prediction of drilling speed in geological core drilling and the automation of geological core drilling machines.

关 键 词：地质岩心钻探 反向传播算法 钻速预测 BP神经网络 ROP 

分 类 号：P634[天文地球—地质矿产勘探]