用隐式非线性方法预测注水井吸水剖面
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摘要
将归一化的地层系数、油水井连通状况、连通油井数、连通井距、量化的砂体类型和措施类型以及渗透级差倒数作为输入参数,相对吸水量作为目标参数,结合支持向量机(SVM)和BP神经网络方法建立隐式非线性吸水剖面预测模型,通过模型的样本学习,建立地质、开发参数和相对吸水量之间的隐式非线性关系。实例分析表明,隐式非线性方法预测吸水剖面精度高且易于扩充模型变量数,要求样本少,更适合矿场应用。
A prediction model of water injection profile using implicit nonlinear method was established combined support vector machine and BP neural network method.In this model,the normalized formation factor,oil and water well connectivity,number of connected wells,connected well space,types of sandstone and stimulations and reciprocal of permeability ratio were taken as input parameters,and the relative intake capacity was taken as target parameter.The implicit nonlinear relationship among geology and production parameters and the relative intake capacity was developed.The results show that this method has high prediction precision for water injection profile,and it is easy to extend the variable number.Under small sample data conditions,this method is more suitable for field application.
A prediction model of water injection profile using implicit nonlinear method was established combined support vector machine and BP neural network method.In this model,the normalized formation factor,oil and water well connectivity,number of connected wells,connected well space,types of sandstone and stimulations and reciprocal of permeability ratio were taken as input parameters,and the relative intake capacity was taken as target parameter.The implicit nonlinear relationship among geology and production parameters and the relative intake capacity was developed.The results show that this method has high prediction precision for water injection profile,and it is easy to extend the variable number.Under small sample data conditions,this method is more suitable for field application.
引文
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[2]张建民,赵永忠,徐晶,等.吸水剖面综合解释方法及应用[J].吉林大学学报:地球科学版,2006,36:143-147.ZHANG Jian-min,ZHAO Yong-zhong,XU Jing,et al.The comprehensive interpretation and application of injec-tion profile log[J].Journal of Jilin University(Earth Sci-ence Edition),2006,36:143-147.
[3]陈林媛.确定吸水剖面的一个简易方法[J].钻采工艺,2003,26(2):93-95.CHEN Lin-yuan.A simple method to identify the injec-tion profile[J].Drilling&Production Technology,2003,26(2):93-95.
[4]单玲,李慧莉,高秀田.利用ANFIS模型预测吸水剖面[J].断块油气田,2009,16(4):72-74.SHAN Ling,LI Hui-li,GAO Xiu-tian.Forecasting injec-tion profile by ANFIS model[J].Fault-Block Oil&GasField,2009,16(4):72-74.
[5]VAPNIK V N.Statistical learning theory[M].New York:John Wiley&Sons Inc,1998:90-100.
[6]杨培杰,印兴耀.基于支持向量机的叠前地震反演方法[J].中国石油大学学报:自然科学版,2008,32(1):37-41.YANG Pei-jie,YIN Xing-yao.Prestack seismic inversionmethod based on support vector machine[J].Journal ofChina University of Petroleum(Edition of Natural Sci-ence),2008,32(1):37-41.
[7]徐长航,陈国明,谢静.基于支持向量机和蒙特卡洛的结构可靠性分析方法及应用[J].中国石油大学学报:自然科学版,2008,32(4):103-108.XU Chang-hang,CHEN Guo-ming,XIE Jing.Structuralreliability analysis method based on support vector ma-chines and Monte Carlo and its application[J].Journal ofChina University of Petroleum(Edition of Natural Sci-ence),2008,32(4):103-108.
[8]王延江,杨培杰,史清江,等.基于支撑向量机的井眼轨迹预测新方法[J].石油大学学报:自然科学版,2005,29(5):50-53.WANG Yan-jiang,YANG Pei-jie,SHI Qing-jiang,et al.Novel wellbore trajectory prediction method based on sup-port vector machine[J].Journal of the University of Pe-troleum,China(Edition of Natural Science),2005,29(5):50-53.
[9]王才经.现代应用数学[M].东营:石油大学出版社,2004:20-60.
[10]KEERTHI S,SHEVADE S,BHATTCHARYYA C.Afast iterative nearest point algorithm for support vectormachine classifier design[J].IEEE Transactions onNeural Network,2000,11(1):124-136.
[11]陈永义,俞小鼎,高学浩,等.处理非线性分类和回归问题的一种新方法(I)——支持向量机方法简介[J].应用气象学报,2004,15(3):345-354.CHEN Yong-yi,YUXiao-ding,GAO Xue-hao,et al.Anew method for non-linear classify and non-linear regres-sionⅠ:introduction to support vector machine[J].Quarterly Journal of Applied Meteorology,2004,15(3):345-354.
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