钻探工程2025，Vol.52Issue(2)：134-143,10.DOI:10.12143/j.ztgc.2025.02.018
基于粒子群优化的融合特征选择钻速预测模型研究
Research on a rate of penetration(ROP)prediction model based on feature selection integrated with particle swarm optimization(PSO)
摘要
Abstract
Traditional rate of penetration(ROP)prediction models have often been constrained by issues such as high data dimensionality and feature correlation,resulting in limited efficiency and accuracy of ROP prediction.To address these issues,a ROP prediction algorithm model based on particle swarm optimization(PSO)with integrated feature selection has been proposed in this paper.Based on data preprocessing,3 key parameters,threshold_1,threshold_2,and threshold_3,have been chosen as optimization targets,and a fitness function has been constructed by combining historical data and the PSO algorithm,thereby establishing the ROP prediction model.Subsequently,the proposed ROP prediction method has been validated using actual drilling data and compared with traditional machine learning algorithm models.Experimental results show that the proposed PSO-based integrated feature selection algorithm achieves higher efficiency and accuracy in feature selection.Compared to before optimization,the accuracy of the 4 machine learning ROP prediction models trained using the optimized integrated feature selection results is improved by 59%,1%,3%,and 1%,respectively.Compared to models trained using all features,the accuracy has been improved by 24%,2%,4%,and 3%,respectively.This paper provides an effective feature selection method for cases where too many feature parameters have been extracted in drilling engineering.It offers significant guidance for the practical application of feature selection algorithms in the engineering field.关键词
钻速预测模型/特征选择/粒子群优化/机器学习Key words
ROP prediction model/feature selection/PSO/machine learning分类
天文与地球科学引用本文复制引用
胥知画,姜杰,周长春,李谦,任军..基于粒子群优化的融合特征选择钻速预测模型研究[J].钻探工程,2025,52(2):134-143,10.基金项目
国家自然科学基金项目"量化月壤扰动特征的模块化月球钻进力学模型研究"(编号:42072344) （编号:42072344）
四川省自然科学基金青年基金项目"基于数字孪生的动态时变钻进工况自适应迁移模型研究"(编号:2024NSFSC0817) （编号:2024NSFSC0817）
