科学技术与工程2024,Vol.24Issue(8) :3207-3214.DOI:10.12404/j.issn.1671-1815.2305024

基于ISSA-SVM的钻井卡钻事故预测

Prediction of Drilling Jam Accidents Based on ISSA-SVM

陈晓 张奇志 王鑫 黄圣杰 陈浩宇
科学技术与工程2024,Vol.24Issue(8) :3207-3214.DOI:10.12404/j.issn.1671-1815.2305024

基于ISSA-SVM的钻井卡钻事故预测

Prediction of Drilling Jam Accidents Based on ISSA-SVM

陈晓 1张奇志 1王鑫 1黄圣杰 1陈浩宇2
扫码查看

作者信息

  • 1. 西安石油大学电子工程学院,西安 710065;陕西省油气井重点测控实验室,西安 710065
  • 2. 西安石油大学新能源学院,西安 710065
  • 折叠

摘要

为预防钻井过程中卡钻事故的发生,通过提出了一种改进麻雀搜索算法(improved sparrow search algorithm,ISSA)优化支持向量机(support vector machines,SVM)的预测模型方法(ISSA-SVM),在发现者位置更新公式中引入一种改进的自适应非线性惯性递减权重;在警戒者位置更新公式中引入莱维飞行策略.利用主成分分析法(principal component analysis,PCA)对外国某大型油田的实测钻井数据进行降维处理,并利用惩罚参数和核参数进行卡钻事故的预测.实验结果表明:ISSA-SVM的预测准确率高达85.185 2%,且收敛速度更快,可见ISSA-SVM可有效预测钻井卡钻事故.

Abstract

In order to prevent the occurrence of stuck drilling accidents during drilling,an improved sparrow search algorithm(ISSA)predictive model method(ISSA-SVM)to optimize support vector machines(SVM)was proposed,and an improved adaptive nonlinear declining inertia weight was introduced into the finder position update formula.Levy flight strategy was introduced into the alert position update formula.The principal component analysis method(PCA)was used to reduce the dimensionality of the measured drilling data of a large foreign oilfield,and the penalty parameters and nuclear parameters were used to predict the stuck drilling accident.The experimental results show that the prediction accuracy of ISSA-SVM is as high as 85.185 2%,and the convergence speed is faster,which shows that ISSA-SVM can effectively predict stuck drilling accidents.

关键词

钻井/卡钻/麻雀搜索算法(SSA)/支持向量机(SVM)/主成分分析法(PCA)

Key words

drilling/stuck drill/sparrow search algorithm(SSA)/support vector machine(SVM)/principal component analysis method(PCA)

引用本文复制引用

基金项目

陕西省科学技术重点研发计划(2017ZDXM-GY-097)

出版年

2024
科学技术与工程
中国技术经济学会

科学技术与工程

CSTPCD北大核心
影响因子:0.338
ISSN:1671-1815
参考文献量17
段落导航相关论文