上海大学学报(自然科学版)2024,Vol.30Issue(5) :968-979.DOI:10.12066/j.issn.1007-2861.2610

基于PSO-SVR算法的预弯BHA钻头合导向力预测方法

Prediction method of combined guiding force of pre-bent BHA bit based on PSO-SVR algorithm

王昭彬 杨赫源 王文昌 陈锋 狄勤丰
上海大学学报(自然科学版)2024,Vol.30Issue(5) :968-979.DOI:10.12066/j.issn.1007-2861.2610

基于PSO-SVR算法的预弯BHA钻头合导向力预测方法

Prediction method of combined guiding force of pre-bent BHA bit based on PSO-SVR algorithm

王昭彬 1杨赫源 1王文昌 1陈锋 2狄勤丰1
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作者信息

  • 1. 上海大学力学与工程科学学院,上海 200444;上海大学上海市应用数学和力学研究所,上海 200072
  • 2. 上海大学机电工程与自动化学院,上海 200444
  • 折叠

摘要

预弯底部钻具组合(bottom hole assembly,BHA)钻头合导向力对于井眼轨迹控制十分重要,但是传统的计算方法耗时耗力.提出了一种基于粒子群算法优化支持向量回归的快速预测预弯BHA钻头合导向力的方法.首先使用加权余量法求解BHA的三维小挠度力学模型,得到钻头合导向力,形成支持向量回归的样本空间;然后,使用粒子群算法优化支持向量回归参数,得到惩罚因子、核函数参数和不敏感系数的最优值;最后,结合实例,使用粒子群算法调参支持向量回归预测钻头合导向力,并对预测结果精度进行分析评价.研究结果表明,预弯BHA钻头合导向力预测精度较高,决定系数R2达0.956 8.

Abstract

The combined guiding force of pre-bent bottom hole assembly(BHA)bit is of paramount significance for well trajectory control,but the traditional calculation method is time-consuming and labor-intensive.A method based on particle swarm optimization and support vector regression is proposed to quickly predict the combined guiding force of pre-bent BHA bit.Firstly,the weighted margin method is used to solve the three-dimensional small deflection mechanical model of the BHA,and the bit combined guiding force is obtained,and the sample space of support vector regression is formed.Secondly,particle swarm optimization is used to optimize the support vector regression parameters,and the optimal values of penalty factor,kernel function parameters and insensitivity coefficient are obtained.Finally,combined with an example,particle swarm optimization is used to predict the bit combined guiding force,and the accuracy of the prediction results is analyzed and evaluated.The results show that the prediction accuracy of the combined guiding force of pre-bent BHA bit is high,and the coefficient of determination R2 is 0.956 8.

关键词

预弯BHA/钻头合导向力/参数优化/粒子群优化/支持向量回归

Key words

pre-bent BHA/bit combined guiding force/parameter optimization/particle swarm optimization/support vector regression

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基金项目

国家自然科学基金资助项目(52174003)

国家自然科学基金资助项目(52374008)

出版年

2024
上海大学学报(自然科学版)
上海大学

上海大学学报(自然科学版)

CSTPCDCSCD北大核心
影响因子:0.579
ISSN:1007-2861
参考文献量18
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