Deep-learning-based surrogate models show great promise for use in geological carbon storage operations. In this work we target an important application — the history matching of storage systems characterized by a high degree of (prior) geological uncertainty. Toward this goal, we extend the recently introduced recurrent R-U-Net surrogate model to treat geomodel realizations drawn from a wide range of geological scenarios. These scenarios are defined by a set of metaparameters, which include the horizontal correlation length, mean and standard deviation of log-permeability, permeability anisotropy ratio, and constants in the porosity-permeability relationship. An infinite number of realizations can be generated for each set of metaparameters, so the range of prior uncertainty is large. The surrogate model is trained with flow simulation results, generated using the open-source simulator GEOS, for 2000 random realizations. The flow problems involve four wells, each injecting 1 Mt CO/year, for 30 years. The trained surrogate model is shown to provide accurate predictions for new realizations over the full range of geological scenarios, with median relative error of 1.3% in pressure and 4.5% in saturation. The surrogate model is incorporated into a hierarchical Markov chain Monte Carlo history matching workflow, where the goal is to generate history matched geomodel realizations and posterior estimates of the metaparameters. We show that, using observed data from monitoring wells in synthetic ‘true’ models, geological uncertainty is reduced substantially. This leads to posterior 3D pressure and saturation fields that display much closer agreement with the true-model responses than do prior predictions.

中文翻译：

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地质CO[公式省略]存储的替代模型及其在分层MCMC历史匹配中的应用

基于深度学习的替代模型在地质碳封存操作中显示出巨大的应用前景。在这项工作中，我们的目标是一个重要的应用——以高度（先前）地质不确定性为特征的存储系统的历史匹配。为了实现这一目标，我们扩展了最近引入的循环 RU-Net 代理模型，以处理从各种地质场景中提取的地理模型实现。这些场景由一组元参数定义，其中包括水平相关长度、测井渗透率的平均值和标准差、渗透率各向异性比以及孔隙度-渗透率关系中的常数。每组元参数可以生成无限数量的实现，因此先验不确定性的范围很大。代理模型使用流模拟结果进行训练，该结果是使用开源模拟器 GEOS 生成的，用于 2000 次随机实现。流动问题涉及四口井，每口井每年注入 1 公吨二氧化碳，持续 30 年。经过训练的替代模型可以为各种地质场景的新认识提供准确的预测，压力中值相对误差为 1.3%，饱和度中值相对误差为 4.5%。代理模型被纳入分层马尔可夫链蒙特卡罗历史匹配工作流程中，其目标是生成历史匹配的地理模型实现和元参数的后验估计。我们表明，使用合成“真实”模型中监测井的观测数据，地质不确定性大大降低。这导致后验 3D 压力和饱和度场与真实模型响应比之前的预测更加一致。