Geological parameterization procedures entail the mapping of a high-dimensional geomodel to a low-dimensional latent variable. These parameterizations can be very useful for history matching because the number of variables to be calibrated is greatly reduced, and the mapping can be constructed such that geological realism is automatically preserved. In this work, a parameterization method based on generative latent diffusion models (LDMs) is developed for 3D channel-levee-mud systems. Geomodels with variable scenario parameters, specifically mud fraction, channel orientation, and channel width, are considered. A perceptual loss term is included during training to improve geological realism. For any set of scenario parameters, an (essentially) infinite number of realizations can be generated, so our LDM parameterizes over a very wide model space. New realizations constructed using the LDM procedure are shown to closely resemble reference geomodels, both visually and in terms of one- and two-point spatial statistics. Flow response distributions, for a specified set of injection and production wells, are also shown to be in close agreement between the two sets of models. The parameterization method is applied for ensemble-based history matching, with model updates performed in the LDM latent space, for cases involving geological scenario uncertainty. For three synthetic true models corresponding to different geological scenarios, we observe clear uncertainty reduction in both production forecasts and geological scenario parameters. The overall method is additionally shown to provide posterior geomodels consistent with the synthetic true model in each case.

翻译：地质参数化方法涉及将高维地质模型映射至低维隐变量空间。此类参数化技术对于历史拟合极为有益，因为待校准变量数量大幅减少，且映射构建方式可自动保持地质真实性。本研究针对三维河道-堤岸-泥岩系统，开发了基于生成式隐扩散模型的参数化方法。研究考虑了具有可变场景参数（具体包括泥岩比例、河道走向及河道宽度）的地质模型。训练过程中引入感知损失项以提升地质真实性。对于任意场景参数组合，均可生成（理论上）无限多个实现样本，因此本隐扩散模型能够对极广的模型空间进行参数化。通过隐扩散流程构建的新实现样本，在视觉特征及一阶/二阶空间统计量方面均与参考地质模型高度吻合。在给定注采井网配置下，两组模型的流动响应分布也呈现高度一致性。该方法进一步应用于集成式历史拟合，在地质场景不确定性的案例中，模型更新操作在隐扩散模型的隐空间内执行。针对对应不同地质场景的三个合成真实模型，我们观察到生产预测与地质场景参数均存在显著的不确定性降低。此外，整体方法在各案例中均能提供与合成真实模型相吻合的后验地质模型。