摘要
对影响井眼轨迹的几个主要因素进行了分析,提出了一种利用小样本统计学习理论中的支撑向量机来进行井眼轨迹预测的新方法,介绍了用于非线性回归估计的支撑向量机的基本原理,通过对一口或几口已钻井的轨迹数据、钻进方式和底部钻具组合结构参数进行学习训练支撑向量机,建立了井眼轨迹的支撑向量机预测模型,并利用多口实钻井的轨迹数据进行了验证。结果表明,这种新方法的预测精度远高于传统的定曲率几何预测方法。
The main factors affecting well trajectory during drilling were analyzed. A novel method for predicting well trajectory by using support vector machine（SVM）based on small samples of statistical learning theory is presented. The basic principles of support vector for regression（SVR）is introduced. A prediction model for well trajectory was established by training the SVR using the data of wellbore trajectory, drilling mode and the structural parameters for the bottom hole assembly of one or several drilled wells. The proposed prediction model was verified with the trajectory data of a number of drilled wells. This model has higher prediction precision than geometric method.
出处
《石油学报》
EI
CAS
CSCD
北大核心
2005年第5期98-101,共4页
Acta Petrolei Sinica
基金
中国石油化工集团公司科技攻关项目"地质导向钻井工艺技术研究"(JP03009)资助
关键词
井眼轨迹
预测模型
支撑向量机
结构参数
统计学习理论
wellbore trajectory
prediction model
support vector machine
structural parameters
statistical learning theory
