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基于流线EnKF油藏自动历史拟合
Automatic history matching of reservoirs using the streamline-based EnKF method
【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。
【Abstract】 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.
【Key words】 automatic history matching; streamline method; ensemble Kalman filter(EnKF); Bayes theory; optimization algorithm;
- 【文献出处】 石油学报 ,Acta Petrolei Sinica , 编辑部邮箱 ,2011年03期
- 【分类号】TE319
- 【被引频次】11
- 【下载频次】435