基于SVM的套管最大von Mises应力预测方法  

An Prediction Method for Determining the Maximum von Mises Stress in Casing Based on SVM

作　　者：狄勤丰[1] 吴志浩 王文昌[1] 覃光煦 陈锋[1] DI Qinfeng;WU Zhihao;WANG Wenchang;QIN Guangxu;CHEN Feng(Institute of Applied Mathematics and Mechanics, Shanghai University, Shanghai,200072,China)

机构地区：[1]上海大学应用数学和力学研究所

出　　处：《石油钻探技术》2019年第3期62-67,共6页Petroleum Drilling Techniques

基　　金：国家自然科学基金重点项目“超深井钻柱非线性动力学及动态安全性基础理论研究”(编号:U1663205),国家自然科学基金青年项目“气体钻井中预弯底部钻具组合控斜的非线性动力学机制”(编号:51704191),国家自然科学基金青年项目“超深井油套管螺纹接头三维力学特性分析及密封机理研究”(编号:51804194)联合资助

摘　　要：为了预测非均匀地应力条件下不居中套管的最大应力,提高套管安全性,研究了基于支持向量机(SVM)的套管最大von Mises应力预测方法.首先确定了影响套管最大应力的关键因素,包括非均匀地应力、水泥环的弹性模量及泊松比、套管偏心距等8个因素;然后利用ANSYS软件构建了套管应力实验样本;最后建立了ε-SVR模型,实现了套管最大应力的预测.通过自学习,基于径向基核函数的SVM回归方法对于训练样本达到了很好的精度,5个测试样本的平均相对误差仅为1.32%,具有较好的预测精度,满足工程需求,且可以实现非均匀地应力条件下不居中套管最大应力的快速求解.研究结果为现场安全施工提供了理论依据.In order to predict the maximum stress of uncentered casing under non-uniform in-situ stress and improve the safety of casing, a prediction method of casing’s maximum von Mises stress based on artificial intelligence SVM is studied. First, the key factors affecting the maximum stress of casing are determined, including non-uniform geologic stress, elastic modulus and Poisson's ratio of cement sheath, eccentricity of casing, etc. Then the "experimental" samples of casing stress are constructed by using ANSYS software. Finally the ε-SVR model is established to realize the prediction of casing’s maximum stress. Through self-learning, the SVM regression method based on RBF kernel achieves good accuracy for training samples. For the five test samples, the average relative error is only 1.32%, which means that this method can meet the needs of engineering application. In particular, this method can be used to quickly solve the maximum stress of uncentered casing under non-uniform in-situ stress.The research results provide theoretical basis for site safety construction.

关 键 词：支持向量机 非均匀地应力 套管偏心距 vonMises应力 

分 类 号：TE21[石油与天然气工程—油气井工程]