Conditional facies modeling combines geological spatial patterns with different types of observed data, to build earth models for predictions of subsurface resources. Recently, researchers have used generative adversarial networks (GANs) for conditional facies modeling, where an unconditional GAN is first trained to learn the geological patterns using the original GAN’s loss function, then appropriate latent vectors are searched to generate facies models that are consistent with the observed conditioning data. A problem with this approach is that the time-consuming search process needs to be conducted for every new conditioning data. As an alternative, we improve GANs for conditional facies simulation (called GANSim) by introducing an extra condition-based loss function and adjusting the architecture of the generator to take the conditioning data as inputs, based on progressive growing of GANs. The condition-based loss function is defined as the inconsistency between the input conditioning value and the corresponding characteristics exhibited by the output facies model, and forces the generator to learn the ability of being consistent with the input conditioning data, together with the learning of geological patterns. Our input conditioning factors include global features (e.g., the mud facies proportion) alone, local features such as sparse well facies data alone, and joint combination of global features and well facies data. After training, we evaluate both the quality of generated facies models and the conditioning ability of the generators, by manual inspection and quantitative assessment. The trained generators are quite robust in generating high-quality facies models conditioned to various types of input conditioning information.

条件相建模将地质空间模式与不同类型的观测数据相结合，以建立用于预测地下资源的地球模型。最近，研究人员已使用生成对抗网络（GAN）进行条件相建模，其中首先训练了无条件GAN以使用原始GAN的损失函数来学习地质模式，然后搜索适当的潜在矢量来生成与岩相一致的相模型。观察到的调节数据。这种方法的问题在于，需要为每个新的条件数据执行耗时的搜索过程。作为备选，我们通过引入额外的基于条件的损失函数，并基于GAN的逐步增长，通过调整生成器的结构以将条件数据作为输入，来改进用于条件相仿真的GAN（称为GANSim）。基于条件的损失函数定义为输入条件值与输出相模型所显示的相应特征之间的不一致，并迫使生成器学习与输入条件数据一致的能力以及对地质条件的学习。模式。我们的输入条件因素包括单独的全局特征（例如泥相比例），局部特征（例如仅稀疏的井相数据）以及全局特征和井相数据的联合组合。训练结束后，通过人工检查和定量评估，我们评估了生成相模型的质量和发生器的调节能力。训练有素的生成器在生成适应各种输入条件信息的条件下的高质量相模型方面非常强大。