Data assimilation in reservoir modeling often involves model variables that are multimodal, such as porosity and permeability. Well established data assimilation methods such as ensemble Kalman filter and ensemble smoother approaches, are based on Gaussian assumptions that are not applicable to multimodal random variables. The selection ensemble smoother is introduced as an alternative to traditional ensemble methods. In the proposed method, the prior distribution of the model variables, for example the porosity field, is a selection-Gaussian distribution, which allows modeling of the multimodal behavior of the posterior ensemble. The proposed approach is applied for validation on a two-dimensional synthetic channelized reservoir. In the application, an unknown reservoir model of porosity and permeability is estimated from the measured data. Seismic and production data are assumed to be repeatedly measured in time and the reservoir model is updated every time new data are assimilated. The example shows that the selection ensemble Kalman model improves the characterisation of the bimodality of the model parameters compared to the results of the ensemble smoother.

储层建模中的数据同化通常涉及多模式的模型变量，例如孔隙度和渗透率。完善的数据同化方法（例如集合卡尔曼滤波器和集合平滑方法）基于不适用于多峰随机变量的高斯假设。介绍了选择合奏平滑器，以替代传统合奏方法。在所提出的方法中，模型变量的先验分布（例如孔隙率场）是选择高斯分布，该模型允许对后合奏的多峰行为进行建模。所提出的方法被用于二维合成通道化油藏的验证。在该应用中，从测得的数据中估算出了未知的孔隙度和渗透率储层模型。假定及时重复测量地震和生产数据，并且每次吸收新数据时都会更新储层模型。该示例表明，与集成平滑器的结果相比，选择集成卡尔曼模型改善了模型参数双峰性的表征。