摘要
为了准确预测西北油田顺北区块固井质量,在固井质量影响因素分析的基础上,采用机器学习方法,建立基于支持向量回归(SVR)模型的固井质量预测模型,并分别利用网格搜索法(GS)、贝叶斯优化算法(BOA)、遗传算法(GA)优选模型惩罚系数C和核函数参数g,以提高SVR预测精度。基于优化的模型结合顺北区块某井进行了实例计算,研究结果表明:相比SVR、GSSVR、BOA-SVR算法,运用GA-SVR算法预测固井质量的均方根误差(RMSE)和平均相对误差(MRE)最低,分别为2.318和7.30%,具有较高的预测精度,可用于该区块固井质量预测。该方法为固井质量预测提供了一种有效手段,有助于固井前开展施工方案优化,提高固井质量。
In order to accurately predict the cementing quality in the Shunbei Block of Northwest Oilfield,a cementing quality prediction model based on support vector regression(SVR)model was established by means of machine learning method,based on the analysis on the influential factors of cementing quality.Then,its penalty coefficient(C)and kernel function parameter(g)were optimized by using grid search method(GS),Bayesian optimization algorithm(BOA)and genetic algorithm(GA),so as to improve SVR prediction accuracy.Finally,the optimized model was used to calculate one certain well of Shunbei Block.The results show that compared with SVR,GS-SVR and BOA-SVR algorithm,GA-SVR algorithm has the lowest the root-mean-square error(RMSE)and mean relative error(MRE)of predicted cementing quality,which are 2.318 and 7.30%,respectively.Obviously its prediction accuracy is higher and it can be used to predict the cementing quality in the Shunbei Block.This method provides an effective means for the prediction of cementing quality and is helpful to optimize the operation scheme before the cementing,so as to improve the cementing quality.
作者
郑双进
程霖
龙震宇
刘洋
赫英状
ZHENG Shuangjin;CHENG Lin;LONG Zhenyu;LIU Yang;HE Yingzhuang(College of Petroleum Engineering,Yangtze University;College of Artificial Intelligence,China University of Petroleum(Beijing);Northeast Sichuan Gas Field,PetroChina Southwest Oil&Gasfield Company;Engineering Technology Research Institute,SINOPEC Northwest Oilfield Company)
出处
《石油钻采工艺》
CAS
北大核心
2021年第4期467-473,共7页
Oil Drilling & Production Technology
基金
国家自然科学基金项目“不规则井眼固井环空流场及井壁附着泥浆滞留机理研究”(编号:51804043)
中国石化西北油田分公司项目“顺北高温高压水平井固井技术研究”(编号:3400007-19-ZC0607-0154)。
关键词
顺北区块
固井质量
支持向量回归
网格搜索法
贝叶斯优化算法
遗传算法
Shunbei Block
cementing quality
support vector regression(SVR)
grid search method(GS)
Bayesian optimization algorithm(BOA)
genetic algorithm(GA)
