摘要
快速获取岩石力学参数和准确识别岩石可钻性是指导不同规模钻进工程(钻井、钻孔)和岩石开挖工程安全施工的重要前提。基于4次微钻实验采集的281组钻进参数和岩石力学参数建立数据库。数据库中的随钻参数包括钻进力(F)、扭矩(T)、转速(N)和钻进速度(V),以此计算出比能(SE)和可钻性指数(Id)。以这些参数为输入参数,采用拟合回归分析和机器学习回归方法预测岩石的单轴抗压强度。此外,根据岩石单轴抗压强度(UCS)、抗拉强度(BTS)、磨蚀指数(CAI)和硬度(HL),通过TOPSIS-RSR方法实现岩石可钻性分类,利用机器学习分类方法感知和识别岩石可钻性。在预测和识别过程中,比较不同方法的精度,确定最优模型。研究方法和结论可为岩石强度的实时原位测量和地层可钻性识别提供新途径,为提高岩石钻进和开挖效率、保障施工安全提供依据。
Rapid acquisition of rock mechanical parameters and accurate identification of rock drillability are important to guide the safe construction of different scale drilling engineering(wells and boreholes)and high-efficient excavation of rock engineering.A database is established based on 281 sets of drilling parameters and rock mechanical parameters collected from four micro drilling experiments.The drilling parameters in the database include drilling force(F),torque(T),rotational speed(N),and rate of penetration(V),from which the specific energy(SE)and the drillability index(Id)are calculated.With these parameters as input,fitting regression analysis and machine learning regression are used to predict the uniaxial compressive strength(UCS)of rocks.Furthermore,TOPSIS-RSR method is used to achieve rock drillability classification,and machine learning classification methods are used to perceive and identify drillability.In the prediction and recognition process,the accuracies of different methods are compared to determine the optimal model.The research methods and findings can provide new approaches for real-time in-situ measurement of UCS and drillability classification of rock,providing a basis for improving the efficiencies of drilling and excavation and ensuring the construction safety.
作者
王少锋
吴毓萌
蔡鑫
周子龙
WANG Shao-feng;WU Yu-meng;CAI Xin;ZHOU Zi-long(School of Resources and Safety Engineering,Central South University,Changsha 410083,China)
基金
Project(52174099)supported by the National Natural Science Foundation of China
Project(2023RC3050)supported by the Science and Technology Innovation Program of Hunan Province,China
Projects(2023ZZTS0497,CX20230210)supported by the Fundamental Research Funds for the Central Universities,China。
