Prediction of the Effect CO2 Immiscible Flooding Based on Support Vector Machine
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摘要: 目前国内缺乏一种快速、准确预测CO2非混相驱油效果的方法,为了解决这一问题,选取剩余地层压力与混相压力之比、孔隙度、渗透率、油藏中深、地层平均有效厚度、地层温度、原油相对密度、含油饱和度、原油黏度、渗透率变异系数、注采比、注入速度和水气交替注入比等13个地质及工程参数作为输入参数,平均单井日增油量作为输出参数构建了预测CO2非混相驱效果的支持向量机预测模型.以国内6个CO2非混相驱项目和1个CO2混相驱项目为学习样本,2个CO2非混相驱项目和1个CO2混相驱项目为检测样本检测了支持向量机预测模型的准确度,结果表明,3个检测样本的预测值与实际值的平均相对误差为5.57%,满足工程要求.利用该模型预测了腰英台油田CO2非混相驱井组的增产效果,与实际增产效果相比,相对误差仅为1.30%.这表明,采用支持向量机方法对CO2非混相驱油效果进行预测可行且有效.Abstract: In order to predict the effect of CO2 immiscible flooding rapidly and accurately, a prediction model based on support vector machine was established. It takes 13 geological and engineering parameters (i.e. the ratio of residual formation pressure and CO2 miscibility pressure, porosity, permeability, reservoir mid-depth, net pay, formation temperature, relative density of crude oil, oil saturation, oil viscosity, coefficient of permeability variation, injection-production ratio, injection rate, and the ratio of water/gas alternating injection) as input parameters, and the average daily oil increment per well as output parameter. with six CO2 immiscible flooding projects and 1 CO2 miscible flooding project as training samples, and two CO2 immiscible flooding projects and one CO2 miscible flooding project as testing samples in China, the accuracy of the model was verified. The results showed that average relative error between predicted value and actual value of above 3 samples was 5.57%, which met the engineering requirement. The model was applied to predict the effect of CO2 immiscible flooding in Yaoyingtai Oilfield, indicating a relative error of only 1.30% in relation with the actual value. It suggested that the method based on support vector machine is feasible and effective to predict the effect of CO2 immiscible flooding.
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[1] 祝春生,程林松.低渗透油藏CO2驱提高原油采收率评价研究[J].钻采工艺,2007,30(6):55-57,60. Zhu Chunsheng,Cheng Linsong.Research on CO2 flooding in low permeability reservoir[J].Drilling Production Technology,2007,30(6):55-57,60. [2] 唐人选,唐小立,秦红祥.注CO2混相驱油藏合理采收率确定[J].石油钻探技术,2012,40(3):112-115. Tang Renxuan,Tang Xiaoli,Qin Hongxiang.Determination of reasonable recovery ratio with CO2 miscible flooding in reservoir[J].Petroleum Drilling Techniques,2012,40(3):112-115. [3] Yin D,Li Y,Zhao D,et al.Long core simulation of advanced CO2 injection in ultra-low permeability reservoir:the Fifth International Conference on Computational and Information Sciences,Shiyang,June 21-23,2013[C].
[4] 王涛.人工神经网络在CO2驱采收率预测中的应用[J].特种油气藏,2011,18(4):77-79. Wang Tao.Application of artificial neural network in recovery factor forecast of carbon dioxide flooding[J].Special Oil Gas Reservoirs,2011,18(4):77-79. [5] 吴晓东,尚庆华,刘长宇,等.一种CO2驱油井产能预测方法及其应用[J].石油钻采工艺,2012,34(1):72-74. Wu Xiaodong,Shang Qinghua,Liu Changyu,et al.A productivity prediction method for CO2 flooding well and its application[J].Oil Drilling Production Technology,2012,34(1):72-74. [6] 肖京男,汪志明,魏建光,等.改进LSSVM在水平井产能预测中的应用[J].石油钻探技术,2010,38(6):95-98. Xiao Jingnan,Wang Zhiming,Wei Jianguang,et al.Application of LSSVM improved horizontal well productivity prediction[J].Petroleum Drilling Techniques,2010,38(6):95-98. [7] 郭永恒.随钻测井曲线预测及更新方法研究[J].石油钻探技术,2010,38(6):25-28. Guo Yongheng.Prediction and update of LWD curve while drilling[J].Petroleum Drilling Techniques,2010,38(6):25-28. [8] 钟仪华,张志银,朱海双.特高含水期油田产量预测新方法[J].断块油气田,2011,18(5):641-644. Zhong Yihua,Zhang Zhiyin,Zhu Haishuang.A new method to predict production of oilfields in ultrahigh water-cut stage[J].Fault-Block Oil Field,20l1,18 (5):641-644. [9] 白晓虎,姜汉桥,王硕亮,等.油田开发动态指标多步预测模型研究[J].断块油气田,2010,17(3):345-347. Bai Xiaohu,Jiang Hanqiao,Wang Shuoliang,et al.Multi-step forecasting model of dynamic index for oilfield development[J].Fault-Block Oil Field,20l0,17 (3):345-347. [10] 陈民锋,白晓虎,郑伟,等.基于支持向量机的早期聚合物驱动态预测研究[J].断块油气田,2012,19(2):199-202. Chen Minfeng,Bai Xiaohu,Zheng Wei,et al.Development index prediction of early polymer flooding based on support vector machine[J].Fault-Block Oil Field,20l2,19 (2):199-202. [11] 肖冬生,乔东生.低阻油层识别新方法及其应用[J].断块油气田,2010,17(4):509-512. Xiao Dongsheng,Qiao Dongsheng.New method for identifying low resistivity reservoirs and its application[J].Fault-Block Oil Field,20l0,17 (4):509-512. [12] 袁士宝,蒋海岩,鲍丙生,等.基于支持向量机的火烧油层效果预测[J].石油勘探与开发,2007,34(1):104-107. Yuan Shibao,Jiang Haiyan,Bao Bingsheng,et al.Effect prediction of in-situ combustion based on support vector machine[J].Petroleum Exploration and Development,2007,34(1):104-107. [13] 李春锦,闫云聚.基于支持向量机的中国石油需求量预测研究[J].西安石油大学学报:自然科学版,2013,28(2):103-106. Li Chunjin,Yan Yunju.Prediction of China’s oil demand based on support vector machine[J].Journal of Xi’an Shiyou University:Natural Science,2013,28(2):103-106. [14] 杨磊,王化增,陈子凌.基于支持向量机的油气储量价值等级评价[J].中国石油大学学报:自然科学版,2012,36(3):192-196. Yang Lei,Wang Huazeng,Chen Ziling.Assessing value classification of oil and gas reserve based on support vector machine[J].Journal of China University of Petroleum:Edition of Natural Science,2012,36(3):192-196. [15] Vapnik V.The nature of statistical learning theory[M].New York:Springer Verlag,1995.
[16] 郑云川.注气提高采收率候选油藏筛选方法及其应用研究[D].南充:西南石油学院石油工程学院,2003. Zheng Yunchuan.Gas injection EOR screening candidate reservoirs method and its application [D].Nanchong:Southwest Petroleum Institute,School of Petroleum Engineering,2003. [17] Rivas O,Embid S,Bolvar F.Ranking reservoirs for carbon dioxide flooding processes[R].SPE 23641,1994.
[18] 雷怀彦,龚承林,官宝聪.注CO2混相驱油藏筛选新方法[J].中国石油大学学报:自然科学版,2008,32(1):72-76. Lei Huaiyan,Gong Chenglin,Guan Baocong.New screening method for reservoir by CO2 injection miscible flooding[J].Journal of China University of Petroleum:Edition of Natural Science,2008,32(1):72-76. [19] 杨佳佳,姜琦刚,陈永良,等.基于最小二乘支持向量机和高分辨率遥感影像的大尺度区域岩性划分[J].中国石油大学学报:自然科学版,2012,36(1):60-67. Yang Jiajia,Jiang Qigang,Chen Yongliang,et al.Lithology division for large-scale region segmentation based on LS-SVM and high resolution remote sensing images[J].Journal of China University of Petroleum:Edition of Natural Science,2012,36(1):60-67. [20] Talbi H,Draa A,Batouche M.A new quantum-inspired genetic algorithm for solving the traveling salesman problem[C]// Proceedings of the IEEE International Conference on Industrial Technology,2004.[S.L.]:IEEE,2004:1192-1197.
[21] 廖海婴.腰英台DB34井区CO2驱替油藏数值模拟研究[J].西安石油大学学报:自然科学版,2010,25(5):50-53. Liao Haiying.Numerical simulation of oil displacement with carbon dioxide in DB34 well block,Yaoyingtai Oilfield[J].Journal of Xi’an Shiyou University:Natural Science,2010,25(5):50-53. -
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