渗透率图像的预测模拟对油田的开发具有重要意义。充分利用条件数据可以提高渗透率模拟的精度，因此提出一种基于连续型多点地质统计法和软硬数据的渗透率图像模拟方法。首先，利用过滤器得分操作对渗透率图像降维，所有的过滤器得分形成了一个过滤器得分空间;其次，通过两步划分法将得分空间划分，得到每个非空得分类的特征，形成一个“特征库”;最后，通过自定义的距离函数从“特征库”提取已知模式的特征，以完成对节点的模拟。比较各情况下渗透率模拟图像可以看出，将软硬数据同时作为条件数据的模拟图像由于采用了较为丰富的条件数据信息，因此与真实情况下的渗透率分布在结构特征上最为接近。

The simulation and prediction of permeability image are quite significant to the development of oil fields. Because the accuracy of permeability can be improved by integrating conditional data including soft data and hard data, a method based on continuous multiple-point geostatistics (MPS) is proposed to improve the accuracy of permeability in a simulated permeability distribution image. First, filters can reduce the dimensions of permeability images to create a “filter score” space. Second, “filter score” space is partitioned by a two-step partition method to obtain the patterns of each non-empty class and forms a “database” of patterns. Finally, a known pattern is drawn from the above “database” of patterns by a self-defined distance function to achieve a simulated value for an unknown node. The simulated images were compared, showing that the structural characteristics of the permeability image simulated by using both soft data and hard data as conditional data are most similar to those of real data from the training image because of the usage of abundant conditional data.