This paper presents a new deep-learning-based generative method applicable to history matching without an inverse scheme. Multiple-point geostatistics is used to construct a prior population stochastically. A convolutional variational autoencoder (VAE) with probabilistic latent space is trained as the generative method, and k-means clustering, nondominated sorting, and multilevel geomodel generations are performed based on flow responses. The applicability of the developed workflow was confirmed using a waterflooding problem with multiple wells in fluvial channel reservoirs. The VAE generates new geomodels based on the latent features and builds equiprobable models neighboring the representative models that reflect the observed production performance. The geomodels match the oil production profiles reliably as the steps progress and accurately forecast the water breakthrough time and liquid production trajectories. The density map of plausible geomodels explains reasonably the uncertainty of channel connectivity. The structural similarity index confirms that the generated geomodels become similar to the target reservoir and thus that the developed VAE-based framework creates geomodels that preserve geological realism. This proposed method involves relatively less time-consuming simulations without any inverse or optimization processes; nonetheless, it generates plausible geomodels in dimensionality-reduced latent space. The study methods and findings are thus applicable to scale-variant data integration and uncertainty assessment.

本文提出了一种新的基于深度学习的生成方法，适用于没有逆向方案的历史匹配。多点地质统计学用于随机构建先验总体。训练具有概率潜在空间的卷积变分自动编码器 (VAE) 作为生成方法，并基于流响应执行 k 均值聚类、非支配排序和多级地理模型生成。所开发工作流程的适用性通过河流河道储层中多口井的注水问题得到证实。VAE 基于潜在特征生成新的地理模型，并在代表模型附近建立等概率模型，以反映观察到的生产性能。随着步骤的进展，地质模型可靠地匹配石油生产剖面，并准确预测水突破时间和液体生产轨迹。似是而非的地质模型的密度图合理地解释了通道连通性的不确定性。结构相似性指数证实生成的地质模型与目标储层相似，因此开发的基于 VAE 的框架创建了保持地质真实性的地质模型。这种提议的方法涉及相对较少耗时的模拟，没有任何逆向或优化过程；尽管如此，它在降维的潜在空间中生成了合理的地理模型。因此，研究方法和结果适用于尺度变量数据整合和不确定性评估。