基于生产动态数据的井间储层连通性识别方法
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摘要
井间储层连通性认识是注水开发油田最重要的基础性研究工作,而目前常用的油藏工程法、数值模拟法和地球化学法,对认识结论可靠性的影响因素较多,尤其对地质资料依赖性较大,且周期长成本高。由注水井、采油井和油水井井间储层组成了一个复杂的非线性注采系统,反映该系统某一侧面的油水井生产动态数据中,隐含着油水井井间储层连通性信息。利用油水井多变量生产动态数据,基于多变量相空间重构方法,可重构这一注采系统动力学特征,通过求取重构注采系统吸引子的关联维,就有可能提取出井间储层连通性信息。使用该方法工作量小,在WR油田应用效果好,得出结论与实际生产相符,且理论清晰算法简单,不需要复杂的数学模型和地质静态模型参数,并可以实现定量解释,为井间储层连通性认识开辟了一条新思路。
Characterization of interwell reservoir connectivity is the most important and fundamental research work to understand water flooding oilfield. Although the commonly used reservoir engineering,numerical simulation and geochemical methods may improve the reliability of interwell reservoir connectivity characterization,they are heavily dependent on extensive collection of geological data and long period observation of well performance through production cycle,which are either expensive or time consuming. The interaction between injectors,producers,and interwell reservoir is a complex nonlinear dynamic system. The production data of oil and water reflect certain aspect of the system,which may hint into the characters of interwell reservoir connectivity. The multivariable facies distribution reconstruction can be applied to resolve the dynamic characteristics of the system based on the multivariable production data of oil and water wells. Thus the reservoir connectivity can be characterized by calculating correlation dimensions of chaotic attractor of the injection-production dynamic system. The amount of work required by this approach is small,and it works well in the application to WR oilfield,with conclusion being consistent with actual production. The approach is clear and simple both in theory and in algorithm. Without complicated mathematical models or parameters of static geological models,it has initiated a new way to quantitatively characterize interwell reservoir connectivity.
Characterization of interwell reservoir connectivity is the most important and fundamental research work to understand water flooding oilfield. Although the commonly used reservoir engineering,numerical simulation and geochemical methods may improve the reliability of interwell reservoir connectivity characterization,they are heavily dependent on extensive collection of geological data and long period observation of well performance through production cycle,which are either expensive or time consuming. The interaction between injectors,producers,and interwell reservoir is a complex nonlinear dynamic system. The production data of oil and water reflect certain aspect of the system,which may hint into the characters of interwell reservoir connectivity. The multivariable facies distribution reconstruction can be applied to resolve the dynamic characteristics of the system based on the multivariable production data of oil and water wells. Thus the reservoir connectivity can be characterized by calculating correlation dimensions of chaotic attractor of the injection-production dynamic system. The amount of work required by this approach is small,and it works well in the application to WR oilfield,with conclusion being consistent with actual production. The approach is clear and simple both in theory and in algorithm. Without complicated mathematical models or parameters of static geological models,it has initiated a new way to quantitatively characterize interwell reservoir connectivity.
引文
[1]逯晓喻,黄志龙,陈践发,等.原油中含氮化合物分布特征与储层连通性研究[J].中国石油大学学报(自然科学版),2012,36(4):19-24.Lu Xiaoyu,Huang Zhilong,Cheng Jianfa,et al. Distribution features of nitrogen compounds in crude oil and reservoir connectivity[J].Journal of China University of Petroleum(Edition of Natural Science),2012,36(4):19-24.
[2]姜瑞忠,李林凯,徐建春,等.低渗透油藏非线性渗流新模型及试井分析[J].石油学报,2012,33(2):264-268.Jiang Ruizhong,Li Linkai,Xu Jianchun,et al. A nonlinear mathematical model for low-permeability reservoirs and well-testing analysis[J]. Acta Petrolei Sinica,2012,33(2):264-268.
[3]刘志斌,任宝生,赵明.基于时变系统的油田产量功能模拟预测[J].西南石油大学学报(自然科学版),2008,30(4):181-184.Liu Zhibin,Ren Baoshen,Zhao Ming. A functional simulation for oilfield output forecast based on time-varying system[J]. Journal of Southwest Petroleum University(Science&Technology Edition),2008,30(4):181-184.
[4]曾玉强,李晓平,黎彬,等.一种简易的注入水流动方向识别方法[J].新疆石油地质,2007,28(2):238-241.Zeng Yuqiang,Li Xiaoping,Li Bin,et al. A simple method for identification of injected water flow direction[J]. Xinjiang Petroleum Geology,2007,28(2):238-241.
[5]赵俊英.基于混沌吸引子形态特征的多相流动力学特性研究[D].天津:天津大学,2013.Zhao Junying. Dynamics characteristics of multiphase flow based on attractor morphology[D]. Tianjin:Tianjin University,2013.
[6] Takens F. Detecting strange attractors in turbulence[C]. Dynamical systems and turbulence,Lecture Notes in Mathematics. New York:Spriger-Verlay,1981:366-381.
[7]赵峰,孙波,张承慧.基于多变量相空间重构和卡尔曼滤波的冷热电联供系统负荷预测方法[J]中国电机工程学报,2016,36(2):399-406.Zhao Feng,Sun Bo,Zhang Chenghui. Cooling,heating and electrical load forecasting method for CCHP system based on multivariate phase space reconstruction and Kalman filter[J]Proceedings of the CSEE,2016,36(2):399-406.
[8]黄发明,殷坤龙,张桂荣,等.基于相空间重构和小波分析-粒子群向量机的滑坡地下水位预测[J].地球科学-中国地质大学学报,2015(7):1254-1265.Huang Faming,Yin Kunlong,Zhang Guirong,et al. Landslide groundwater level time series prediction based on phase space reconstruction and wavelet analysis-support vector machine optimized by PSO algorithm[J]. Earth Science-Journal of China University of Geosciences,2015(7):1254-1265.
[9] Hastings S P,Troy W C. A shooting approach to chaos in the Lorenz equations[J]. Journal of Differential Equations,1996,127(1):41-53.
[10]岳毅宏,韩文秀,程国平.多变量时间序列相空间重构中参数的确定[J].控制与决策,2005,20(3):290-293.Yue Yihong,Han Wenxiu,Cheng Guoping. Determination of parameters in the phase-space reconstruction of multivariate time series[J].Control and Decision,2005,20(3):290-293.
[11] Grassberger P,Procaccia I. Characterization of strange attractors[J].Physical Review Letters,1983,50(5):346.
[12]付强,李晨溪,张朝曦.关于G-P算法计算混沌关联维的讨论[J].解放军理工大学学报(自然科学版),2014(3):275-282.Fu Qiang,Li chengxi,Zhang Zhaoxi. G-P algorithm for evaluating the correlation dimension in chaos[J]. Journal of PLA University of Science and Technology(Natural Science Edition),2014(3):275-282.
[13]吴云,周硕愚.多时间序列求关联维D2[J].大地测量与地球动力学,1995(1):20-25.Wu Yun Zhou Shuoyu. Determining correlation dimension from multiple time sequences D2[J]. Journal of Geodesy and Geodynamics,1995(1):20-25.
[14]张春涛,马千里,彭宏.基于信息熵优化相空间重构参数的混沌时间序列预测[J].物理学报,2010,59(11):7623-7629.Zhang Chuntao,Ma Qianli,Peng Hong. Chaotic time series prediction based on information entropy optimized parameters of phase space reconstruction[J]. Acta Physica Sinica,2010,59(11):7623-7629.
[15]张艳兰,栾元重,尹燕运,等.混沌时间序列相空间重构及特性识别[J].测绘科学,2016,41(4):15-18.Zhang Yanlan,Luan Yuanchong,Yin Yanyun,et al. The deformation monitoring of chaotic time series phase space reconstruction and feature recognition[J]. Science of Surveying and Mapping,2016,41(4):15-18.
[16]王振洲,宁新宝.同步十二导联心电图信号的关联维分布[J].科学通报,2000,45(8):873-877.Wang Zhengzhou,Ning Xinbao. The distribution of the correlation dimension of the synchronous 12-lead ecg signals[J]. Chinese Science Bulletin,2000,45(8):873-877.
[17]杨志安.用等间距分格子法计算互信息函数确定延迟时间[J].计算物理,1995,12(4):442-448.Yang Zhian. Determination of delay time by calculating mutual information with equally distant space cells[J]. Chinese Journal of Computational Physics,1995,12(4):442-448.
[18]杜柳青,殷国富,余永维.基于混沌相空间重构的数控机床运动精度预测[J].农业机械学报,2015,46(10):397-402.Du Liuqing,Yin Guofu,Yu Yongwei. Prediction of numerical control machine’s motion precision based on chaotic phase space reconstruction[J]. Transactions of the Chinese Society for Agricultural Machinery,2015,46(10):397-402.
[19]刘艳辉,王芳,朱文超,等.用Cao方法探讨ENSO可预报性的年代际变化[J].气象水文海洋仪器,2013,30(2):49-54.Liu Yanhui,Wang Fang,Zhu Wenchao,et al. Research on decadal variability of ENSO predictability with Cao method[J]. Meteorological,Hydrological and Marine Instruments,2013,30(2):49-54.
[20]于江龙,彭跃华.应用互信息法和Cao方法探讨ENSO的可预报性[J].气象科技,2011,39(1):9-12.Yu Jianglong,Peng Yuehua. Predictability study of ENSO with mutual information and Cao methods[J]. Meteorological Science and Technology,2011,39(1):9-12.
[21]樊重俊.多个动态序列之间非线性相关性度量方法[J].系统工程学报,2010,25(4):433-473.Fan Chongjun. Measuring approach for nonlinear dependence among time series[J]. Journal of Systems Engineering,2010,25(4):433-473.
[2]姜瑞忠,李林凯,徐建春,等.低渗透油藏非线性渗流新模型及试井分析[J].石油学报,2012,33(2):264-268.Jiang Ruizhong,Li Linkai,Xu Jianchun,et al. A nonlinear mathematical model for low-permeability reservoirs and well-testing analysis[J]. Acta Petrolei Sinica,2012,33(2):264-268.
[3]刘志斌,任宝生,赵明.基于时变系统的油田产量功能模拟预测[J].西南石油大学学报(自然科学版),2008,30(4):181-184.Liu Zhibin,Ren Baoshen,Zhao Ming. A functional simulation for oilfield output forecast based on time-varying system[J]. Journal of Southwest Petroleum University(Science&Technology Edition),2008,30(4):181-184.
[4]曾玉强,李晓平,黎彬,等.一种简易的注入水流动方向识别方法[J].新疆石油地质,2007,28(2):238-241.Zeng Yuqiang,Li Xiaoping,Li Bin,et al. A simple method for identification of injected water flow direction[J]. Xinjiang Petroleum Geology,2007,28(2):238-241.
[5]赵俊英.基于混沌吸引子形态特征的多相流动力学特性研究[D].天津:天津大学,2013.Zhao Junying. Dynamics characteristics of multiphase flow based on attractor morphology[D]. Tianjin:Tianjin University,2013.
[6] Takens F. Detecting strange attractors in turbulence[C]. Dynamical systems and turbulence,Lecture Notes in Mathematics. New York:Spriger-Verlay,1981:366-381.
[7]赵峰,孙波,张承慧.基于多变量相空间重构和卡尔曼滤波的冷热电联供系统负荷预测方法[J]中国电机工程学报,2016,36(2):399-406.Zhao Feng,Sun Bo,Zhang Chenghui. Cooling,heating and electrical load forecasting method for CCHP system based on multivariate phase space reconstruction and Kalman filter[J]Proceedings of the CSEE,2016,36(2):399-406.
[8]黄发明,殷坤龙,张桂荣,等.基于相空间重构和小波分析-粒子群向量机的滑坡地下水位预测[J].地球科学-中国地质大学学报,2015(7):1254-1265.Huang Faming,Yin Kunlong,Zhang Guirong,et al. Landslide groundwater level time series prediction based on phase space reconstruction and wavelet analysis-support vector machine optimized by PSO algorithm[J]. Earth Science-Journal of China University of Geosciences,2015(7):1254-1265.
[9] Hastings S P,Troy W C. A shooting approach to chaos in the Lorenz equations[J]. Journal of Differential Equations,1996,127(1):41-53.
[10]岳毅宏,韩文秀,程国平.多变量时间序列相空间重构中参数的确定[J].控制与决策,2005,20(3):290-293.Yue Yihong,Han Wenxiu,Cheng Guoping. Determination of parameters in the phase-space reconstruction of multivariate time series[J].Control and Decision,2005,20(3):290-293.
[11] Grassberger P,Procaccia I. Characterization of strange attractors[J].Physical Review Letters,1983,50(5):346.
[12]付强,李晨溪,张朝曦.关于G-P算法计算混沌关联维的讨论[J].解放军理工大学学报(自然科学版),2014(3):275-282.Fu Qiang,Li chengxi,Zhang Zhaoxi. G-P algorithm for evaluating the correlation dimension in chaos[J]. Journal of PLA University of Science and Technology(Natural Science Edition),2014(3):275-282.
[13]吴云,周硕愚.多时间序列求关联维D2[J].大地测量与地球动力学,1995(1):20-25.Wu Yun Zhou Shuoyu. Determining correlation dimension from multiple time sequences D2[J]. Journal of Geodesy and Geodynamics,1995(1):20-25.
[14]张春涛,马千里,彭宏.基于信息熵优化相空间重构参数的混沌时间序列预测[J].物理学报,2010,59(11):7623-7629.Zhang Chuntao,Ma Qianli,Peng Hong. Chaotic time series prediction based on information entropy optimized parameters of phase space reconstruction[J]. Acta Physica Sinica,2010,59(11):7623-7629.
[15]张艳兰,栾元重,尹燕运,等.混沌时间序列相空间重构及特性识别[J].测绘科学,2016,41(4):15-18.Zhang Yanlan,Luan Yuanchong,Yin Yanyun,et al. The deformation monitoring of chaotic time series phase space reconstruction and feature recognition[J]. Science of Surveying and Mapping,2016,41(4):15-18.
[16]王振洲,宁新宝.同步十二导联心电图信号的关联维分布[J].科学通报,2000,45(8):873-877.Wang Zhengzhou,Ning Xinbao. The distribution of the correlation dimension of the synchronous 12-lead ecg signals[J]. Chinese Science Bulletin,2000,45(8):873-877.
[17]杨志安.用等间距分格子法计算互信息函数确定延迟时间[J].计算物理,1995,12(4):442-448.Yang Zhian. Determination of delay time by calculating mutual information with equally distant space cells[J]. Chinese Journal of Computational Physics,1995,12(4):442-448.
[18]杜柳青,殷国富,余永维.基于混沌相空间重构的数控机床运动精度预测[J].农业机械学报,2015,46(10):397-402.Du Liuqing,Yin Guofu,Yu Yongwei. Prediction of numerical control machine’s motion precision based on chaotic phase space reconstruction[J]. Transactions of the Chinese Society for Agricultural Machinery,2015,46(10):397-402.
[19]刘艳辉,王芳,朱文超,等.用Cao方法探讨ENSO可预报性的年代际变化[J].气象水文海洋仪器,2013,30(2):49-54.Liu Yanhui,Wang Fang,Zhu Wenchao,et al. Research on decadal variability of ENSO predictability with Cao method[J]. Meteorological,Hydrological and Marine Instruments,2013,30(2):49-54.
[20]于江龙,彭跃华.应用互信息法和Cao方法探讨ENSO的可预报性[J].气象科技,2011,39(1):9-12.Yu Jianglong,Peng Yuehua. Predictability study of ENSO with mutual information and Cao methods[J]. Meteorological Science and Technology,2011,39(1):9-12.
[21]樊重俊.多个动态序列之间非线性相关性度量方法[J].系统工程学报,2010,25(4):433-473.Fan Chongjun. Measuring approach for nonlinear dependence among time series[J]. Journal of Systems Engineering,2010,25(4):433-473.