标题： 不确定性地质情景下的历史匹配，采用生成深度学习和图卷积的隐式地质现实控制 显示英文标题

标题： History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions

摘要： 基于图的变分自编码器是一种能够通过低维潜在空间的概念处理不同地质情景（如沉积或构造）不确定性的架构。 与近期研究的主要区别在于，在储层建模中使用了基于图的方法，而不是更传统的基于网格的深度学习方法。 我们提供了一种通过生成模型的潜在变量和测地线度量来隐式控制地质真实性的解决方案。 我们使用合成数据集进行AHM实验，该数据集包含具有两种不同情景（一个通道和两个通道）的渠道化地质表示的3D实现，展示了该方法的可行性。 我们使用PCA、t-SNE和TDA等工具对潜在空间进行深入分析，以说明其结构。 显示英文摘要

摘要： The graph-based variational autoencoder represents an architecture that can handle the uncertainty of different geological scenarios, such as depositional or structural, through the concept of a lowerdimensional latent space. The main difference from recent studies is utilisation of a graph-based approach in reservoir modelling instead of the more traditional lattice-based deep learning methods. We provide a solution to implicitly control the geological realism through the latent variables of a generative model and Geodesic metrics. Our experiments of AHM with synthetic dataset that consists of 3D realisations of channelised geological representations with two distinct scenarios with one and two channels shows the viability of the approach. We offer in-depth analysis of the latent space using tools such as PCA, t-SNE, and TDA to illustrate its structure.