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 CO2/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.

翻译：基于深度学习的代理模型在地质碳封存操作中展现出巨大潜力。本研究针对一个重要应用——具有高度（先验）地质不确定性的封存系统的历史匹配。为此，我们扩展了近期提出的循环R-U-Net代理模型，使其能够处理从广泛地质情景中抽取的地质模型实现。这些情景由一组元参数定义，包括水平相关长度、对数渗透率的均值与标准差、渗透率各向异性比，以及孔隙度-渗透率关系中的常数。对于每组元参数可生成无限数量的实现，因此先验不确定性范围较大。代理模型基于开源模拟器GEOS生成的2000个随机实现的流动模拟结果进行训练。流动问题涉及四口井，每口井以每年注入100万吨CO2的速度持续30年。结果显示，经过训练的代理模型能够对全部地质情景范围内的新实现提供准确预测，压力中位相对误差为1.3%，饱和度中位相对误差为4.5%。该代理模型被集成到分层马尔可夫链蒙特卡洛历史匹配工作流中，其目标是生成历史匹配的地质模型实现及元参数的后验估计。研究表明，利用合成"真实"模型中监测井的观测数据，地质不确定性得到显著降低。由此得到的后验三维压力场和饱和度场，与真实模型响应之间的吻合程度远优于先验预测。