Subsurface models are central pieces of information in different earth-related disciplines such as groundwater management and hydrocarbon reservoir characterization. These models are normally obtained using geostatistical simulation methods. Recently, methods based on deep learning algorithms have been applied as subsurface model generators. However, there are still challenges on how to include conditioning data and ensure model variability within a set of realizations. We illustrate the potential of Generative Adversarial Networks (GANs) to create unconditional and conditional facies models. Based on a synthetic facies dataset, we first train a Deep Convolution GAN (DCGAN) to produce unconditional facies models. Then, we show how image-to-image translation based on a U-Net GAN framework, including noise-layers, content loss function and diversity loss function, is used to model conditioning geological facies. Results show that GANs are powerful models to capture complex geological facies patterns and to generate facies realizations indistinguishable from the ones comprising the training dataset. The U-Net GAN framework performs well in providing variable models while honoring conditioning data in several scenarios. The results shown herein are expected to spark a new generation of methods for subsurface geological facies with fragmentary measurements.

地下模型是与地球有关的不同学科的核心信息，例如地下水管理和油气藏表征。这些模型通常使用地统计模拟方法获得。最近，基于深度学习算法的方法已被用作地下模型生成器。但是，如何在一组实现中包括条件数据并确保模型可变性仍然存在挑战。我们说明了生成对抗网络（GAN）创造无条件和条件相模型的潜力。基于合成相数据集，我们首先训练深度卷积GAN（DCGAN）以产生无条件相模型。然后，我们展示如何基于U-Net GAN框架进行图像到图像的转换，包括噪声层，含量损失函数和多样性损失函数，用于对条件地质相进行建模。结果表明，GANs是捕获复杂地质相图样并生成与组成训练数据集的相差无几的相变实现的强大模型。在提供可变模型的同时，U-Net GAN框架在遵守几种情况下的条件数据的同时表现出色。预期本文显示的结果将引发用于分段测量的地下地质相的新一代方法。在提供可变模型的同时，U-Net GAN框架在遵守几种情况下的条件数据的同时表现出色。预期本文显示的结果将引发用于分段测量的地下地质相的新一代方法。在提供可变模型的同时，U-Net GAN框架在遵守几种情况下的条件数据的同时表现出色。预期本文显示的结果将引发用于分段测量的地下地质相的新一代方法。