用生成模型隐式控制地质真实感，提升不确定性下的历史拟合效果。
History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions
- 基于图结构的变分自编码器建模地质场景不确定性。
- 在含1~2条河道的合成数据上实现高精度历史拟合。
- 通过潜空间分析揭示地质特征的可解释性结构，适合油藏模拟研究者。
基于图的变分自编码器通过低维潜空间处理不同地质场景（如沉积或构造）的不确定性。与传统网格方法不同，该方法采用图结构进行储层建模。通过生成模型的潜变量和测地线度量，实现对地质真实感的隐式控制。在包含三维河道化地质表征的合成数据集上，针对含一条或两条河道的两种场景进行历史拟合实验，验证了方法的有效性。利用PCA、t-SNE和拓扑数据分析工具深入剖析潜空间结构，揭示其内在规律。
原文摘要 · Abstract (English)
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.
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