钻探工程2024，Vol.51Issue(5)：45-52,8.DOI:10.12143/j.ztgc.2024.05.005
大洋钻探过程钻速在线区间预测方法——以微型钻探船室内模拟实验为例
An online interval prediction method for rate of penetration(ROP)during the ocean drilling:A case study on the indoor simulation experiments by micro drilling ship
摘要
Abstract
Ocean drilling is a major means for the exploration and development of marine energy resources and the study of crustal tectonic evolution,which is often faced with problems such as large disturbances in complex sea state and strong uncertainty in seafloor formation.This paper proposes an online interval prediction method for ROP during the ocean drilling process,and carry out the validation of the method with the indoor simulation experiment of micro drilling ship as an example,so as to lay an important foundation for the engineering application.Firstly,methods such as data resampling,data time-depth matching and data filtering are applied to pre-process the multi-source ocean drilling data.Secondly,methods such as Extreme Learning Machine(ELM)and Particle Swarm Optimization(PSO)are applied to establish a point prediction model of ROP.Furthermore,nonparametric estimation method is utilized to construct confidence intervals,establish ROP interval prediction model,and carry out ROP interval prediction.Finally,the interval prediction model parameters are updated online by moving window to realize online learning and optimization of the model.The comparative results of the indoor simulation experiments on the micro drilling ship verify that the proposed method has strong ROP prediction capability and robustness,and can provide a new engineering solution for the optimization and control of ROP during the ocean drilling process.关键词
大洋钻探过程/钻速区间预测/室内模拟实验/在线学习/误差分析Key words
ocean drilling process/ROP prediction/indoor simulation experiments/online learning/error analysis分类
地质学引用本文复制引用
张琦,甘超,曹卫华..大洋钻探过程钻速在线区间预测方法——以微型钻探船室内模拟实验为例[J].钻探工程,2024,51(5):45-52,8.基金项目
国家自然科学基金面上项目"复杂地质钻进过程效率动态优化与安全智能预警"(编号:62173313) （编号:62173313）
"111计划"项目(编号:B17040) （编号:B17040）
