基于流线EnKF油藏自动历史拟合
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摘要
作为一种油藏参数动态估计的优化算法,将集合卡尔曼滤波EnKF与流线方法相结合,在算法实现过程中,选择具有更高计算精度和更快计算速度的流线模拟器进行预测,将油藏模型自动还原为一系列沿流线的一维模型,使数值弥散和受网格划分的影响达到最小,保持了明显的驱替前缘。采用序贯高斯协模拟方法生成一组地质模型,通过实时观测数据(产量及含水率等),连续动态更新油藏模型的静态参数(渗透率及孔隙度等)、动态参数(压力及饱和度等),同时实现流线分布的更新,直观地反映油藏流体在注采井之间的运动轨迹。采用Bayes理论阐述了流线EnKF数学原理,并通过拟合计算一个二维水驱油藏模型,验证了方法的有效性。
The present paper combined ensenble Kalman filter(EnkF) with a streamline method as an optimization algorithm for the dynamic estimation of reservoir parameters.In the realization of this algorithm,a streamline simulator with higher accuracy and faster computing speed was chosen to predict the performance,and a reservoir model was automatically decoupled into a series of one-dimensional models along streamlines,which could minimize the numerical dispersion and the effect of grid generation while maintaining a sharp displacement front.A group of geologic models were generated by using the sequential Gaussian co-simulation method,and by means of assimilating the real-time observation data(output,water cut,etc.),both of the static parameters(porosity,permeability,etc.) and the dynamic parameters(pressure,saturation,etc.) of a reservoir model were updated dynamically and continuously,in the meantime,the streamline distribution was also updated,thus,moving traces of reservoir fluids between injection and production wells could be visually reflected.The mathematical principle of the streamline-based EnKF method was elaborated by the Bayes theory,and the validity of this method was verified by means of fitting calculation of a 2D water-drive reservoir model.
The present paper combined ensenble Kalman filter(EnkF) with a streamline method as an optimization algorithm for the dynamic estimation of reservoir parameters.In the realization of this algorithm,a streamline simulator with higher accuracy and faster computing speed was chosen to predict the performance,and a reservoir model was automatically decoupled into a series of one-dimensional models along streamlines,which could minimize the numerical dispersion and the effect of grid generation while maintaining a sharp displacement front.A group of geologic models were generated by using the sequential Gaussian co-simulation method,and by means of assimilating the real-time observation data(output,water cut,etc.),both of the static parameters(porosity,permeability,etc.) and the dynamic parameters(pressure,saturation,etc.) of a reservoir model were updated dynamically and continuously,in the meantime,the streamline distribution was also updated,thus,moving traces of reservoir fluids between injection and production wells could be visually reflected.The mathematical principle of the streamline-based EnKF method was elaborated by the Bayes theory,and the validity of this method was verified by means of fitting calculation of a 2D water-drive reservoir model.
引文
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[13]叶继根,吴向红,朱怡翔,等.大规模角点网格计算机辅助油藏模拟历史拟合方法研究[J].石油学报,2007,28(2):83-86.Ye Jigen,Wu Xianghong,Zhu Yixiang,et al.Study on computerassisted history-matching method in corner point grids[J].ActaPetrolei Sinica,2007,28(2):83-86.
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[15]Datta-Gupta A,King M J.A semianalytic approach to tracer flowmodeling in heterogeneous permeable media[J].Advances inWater Resources,1995,18(1):9-24.
[2]Wang C H,Li G M,Reynolds A C.Production optimization inclosed-loop reservoir management[R].SPE109805,2007.
[3]Chen Y,Oliver D,Zhang D X.Efficient ensemble-based closed-loop production optimization[R].SPE112873,2008.
[4]Rolf J L,Shafieirad A,Naevdal G.Closed loop reservoir manage-ment using the Ensemble Kalman filter and sequential quadraticprogramming[R].SPE119101,2009.
[5]Bianco A,Cominelli A,Dovera L.History matching and produc-tion forecast uncertainty by means of the Ensemble Kalman fil-ter:A real field application[R].SPE107161,2007.
[6]Zhang Y F,Oliver S.History matching using a hierarchical sto-chastic model with the Ensemble Kalman filter:A field case stud-y[R].SPE118879,2009.
[7]Arroyo-Negrete E,Deepak D,Datta-Gupta A,et al.Streamline-assisted Ensemble Kalman filter for rapid and continuous reser-voir model updating[R].SPE104255,2008.
[8]Zafari M,Reynolds A C.Assessing the uncertainty in reservoirdescription and performance predictions with the Ensemble Kal-man filter[R].SPE95750,2007.
[9]陈玉栋,黄旭日.EnKF整合三维地震数据和动态数据的应用[J].勘探地球物理进展,2009,32(2):138-146.Chen Yudong,Huang Xuri.Application of assimilating-3D seis-mic data with dynamic data using Ensemble Kalman filter[J].Progress in Exploration Geophysics,2009,32(2):138-146.
[10]张巍,邸元,Wu Yushu.基于集合卡尔曼滤波的油藏辅助历史拟合[J].大庆石油学院学报,2009,33(5):74-78.Zhang Wei,Di Yuan,Wu Yushu,et al.Assisted history matchingmethod based on ensemble Kalman filter[J].Journal of DaqingPetroleum Institute,2009,33(5):74-78.
[11]陈兆芳,张建荣,陈月明,等.油藏数值模拟自动历史拟合方法研究及应用[J].石油勘探与开发,2003,30(4):82-84.Chen Zhaofang,Zhang Jianrong,Chen Yueming,et al.Automatichistorical matching method and its application in reservoir simu-lation[J].Petroleum Exploration and Development,2003,30(4):82-84.
[12]程时清,王志伟,李相方,等.应用渗流反问题理论计算非均质油气藏多参数分布[J].石油学报,2006,27(3):87-90.Cheng Shiqing,Wang Zhiwei,Li Xiangfang,et al.Calculation ofmulti-parameter distribution in heterogeneous reservoir based onthe inverse problem theory[J].Acta Petrolei Sinica,2006,27(3):87-90.
[13]叶继根,吴向红,朱怡翔,等.大规模角点网格计算机辅助油藏模拟历史拟合方法研究[J].石油学报,2007,28(2):83-86.Ye Jigen,Wu Xianghong,Zhu Yixiang,et al.Study on computerassisted history-matching method in corner point grids[J].ActaPetrolei Sinica,2007,28(2):83-86.
[14]王洪宝,苏振阁,陈忠云.油藏水驱开发三维流线模型[J].石油勘探与开发,2004,31(2):99-103.Wang Hongbao,Su Zhenge,Chen Zhongyun.A-3D streamlinemodel in waterflooding reservoir[J].Petroleum Exploration andDevelopment,2004,31(2):99-103.
[15]Datta-Gupta A,King M J.A semianalytic approach to tracer flowmodeling in heterogeneous permeable media[J].Advances inWater Resources,1995,18(1):9-24.
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