Comparing the adaptive Gaussian mixture filter with the ensemble Kalman filter on synthetic reservoir models
Comparing the adaptive Gaussian mixture filter with the ensemble Kalman filter on synthetic reservoir models
复制标题
在合成油藏模型上比较自适应高斯混合滤波器与集成卡尔曼滤波器
DOI：
10.1007/s10596-011-9262-2
复制
发表时间：
2012
影响因子：
2.5
通讯作者：
H. Skaug
中科院分区：
文献类型：
--
作者：
A. Stordal;R. Valestrand;H. A. Karlsen;G. Nævdal;H. Skaug
在过去的几年里，集合卡尔曼滤波器（EnKF）已成为一种非常流行的石油储层历史匹配工具。 EnKF 是更传统的历史匹配技术的替代方案，因为它计算速度快且易于实现。 EnKF 不是寻求最佳模型估计，而是一种蒙特卡罗方法，用状态向量集合表示解。最近，人们提出了几种基于集成的方法来改进 EnKF 生成的解决方案。在本文中，我们在 2D 合成油藏和 Punq-S3 测试用例上将 EnKF 与最近提出的方法之一——自适应高斯混合滤波器（AGM）进行比较。引入 AGM 是为了放宽 EnKF 中隐式表述的高斯先验分布的要求。通过将粒子滤波器的思想与 EnKF 相结合，AGM 扩展了低秩核粒子卡尔曼滤波器。模拟研究表明，虽然两种方法都很好地匹配了历史数据，但 AGM 更能保留先验分布的地质统计数据。此外，AGM 还生成估计字段，与使用 EnKF 获得的相应字段相比，该估计字段与参考字段具有更高的经验相关性。
Over the last years, the ensemble Kalman filter (EnKF) has become a very popular tool for history matching petroleum reservoirs. EnKF is an alternative to more traditional history matching techniques as it is computationally fast and easy to implement. Instead of seeking one best model estimate, EnKF is a Monte Carlo method that represents the solution with an ensemble of state vectors. Lately, several ensemble-based methods have been proposed to improve upon the solution produced by EnKF. In this paper, we compare EnKF with one of the most recently proposed methods, the adaptive Gaussian mixture filter (AGM), on a 2D synthetic reservoir and the Punq-S3 test case. AGM was introduced to loosen up the requirement of a Gaussian prior distribution as implicitly formulated in EnKF. By combining ideas from particle filters with EnKF, AGM extends the low-rank kernel particle Kalman filter. The simulation study shows that while both methods match the historical data well, AGM is better at preserving the geostatistics of the prior distribution. Further, AGM also produces estimated fields that have a higher empirical correlation with the reference field than the corresponding fields obtained with EnKF.
影响因子：
4
作者：
Apte, A.;Hairer, M.;Voss, J.
通讯作者：
Voss, J.