In recent years, multiple-point geostatistical (MPS) approaches have gained significant popularity for modeling subsurface heterogeneity in hydrogeological systems by employing a training image for describing the features of the target field. The most important challenges of MPS simulation methods include appropriate pattern reproduction and connectivity preservation, handling conditional data, and appropriately modeling the variability of real fields. Preserving connectivity of the patterns is of paramount importance, particularly in fluid flow modeling problems. During sequential simulation, if the algorithm produces a value (or patch) inconsistently with previously synthesized data, such conflicts will propagate in the realization and lead to poor pattern reproduction. Here, we have introduced a two-step simulation algorithm, where in the first phase, the coarse structure of the realization is synthesized with minimum conflicts by rejecting inconsistent patterns and allowing removing previously synthesized data, and in the second phase, the fine grid is simulated by ignoring the conflicts. Ignoring short-range inconsistencies in the fine simulation phase not only improves the algorithm’s convergence but also leads to higher variabilities without sacrificing the quality of the realizations. Convergence problems of traditional conflict-handling methods are further alleviated by a new distance reweighting strategy, which prevents cyclic deletions and resimulations. We have employed different statistical descriptors to evaluate our method in comparison with existing pixel and patch-based methods in conditional and unconditional modes. The proposed method shows outstanding results in terms of connectivity preservation, conditional data handling, and pattern innovation. Compared to traditional conflict-handling methods, the proposed method shows good convergence and histogram preservation.

近年来，多点地统计（MPS）方法通过采用用于描述目标场特征的训练图像来模拟水文地质系统中的地下非均质性而获得了广泛的应用。MPS仿真方法的最重要挑战包括适当的模式再现和连接保留，处理条件数据以及对真实字段的可变性进行适当建模。保持模式的连通性至关重要，尤其是在流体流动建模问题中。在顺序仿真期间，如果算法产生的值（或补丁）与先前合成的数据不一致，则此类冲突将在实现中传播，并导致不良的图案再现。在这里，我们介绍了两步仿真算法，其中在第一阶段，通过拒绝不一致的模式并允许删除先前合成的数据，以最小的冲突来合成实现的粗略结构，而在第二阶段，通过忽略冲突来模拟精细网格。在精细仿真阶段忽略短距离不一致性，不仅可以提高算法的收敛性，而且可以在不牺牲实现质量的情况下提高可变性。一种新的距离重新加权策略可以进一步缓解传统冲突处理方法的收敛性问题，该策略可以防止循环删除和重新模拟。与已有的基于像素和基于补丁的方法在条件和非条件模式下相比，我们采用了不同的统计描述符来评估我们的方法。所提出的方法在连通性保存，条件数据处理和模式创新方面显示了出色的结果。与传统的冲突处理方法相比，该方法具有良好的收敛性和直方图保存性。