Deep learning surrogate modeling shows great promise for subsurface flow applications, but the training demands can be substantial. Here we introduce a new surrogate modeling framework to predict CO2 saturation, pressure and surface displacement for use in the history matching of carbon storage operations. Rather than train using a large number of expensive coupled flow-geomechanics simulation runs, training here involves a large number of inexpensive flow-only simulations combined with a much smaller number of coupled runs. The flow-only runs use an effective rock compressibility, which is shown to provide accurate predictions for saturation and pressure for our system. A recurrent residual U-Net architecture is applied for the saturation and pressure surrogate models, while a new residual U-Net model is introduced to predict surface displacement. The surface displacement surrogate accepts, as inputs, geomodel quantities along with saturation and pressure surrogate predictions. Median relative error for a diverse test set is less than 4% for all variables. The surrogate models are incorporated into a hierarchical Markov chain Monte Carlo history matching workflow. Surrogate error is included using a new treatment involving the full model error covariance matrix. A high degree of prior uncertainty, with geomodels characterized by uncertain geological scenario parameters (metaparameters) and associated realizations, is considered. History matching results for a synthetic true model are generated using in-situ monitoring-well data only, surface displacement data only, and both data types. The enhanced uncertainty reduction achieved with both data types is quantified. Posterior saturation and surface displacement fields are shown to correspond well with the true solution.

翻译：深度学习代理建模在地下流动应用中展现出巨大潜力，但其训练需求可能十分庞大。本文提出一种新的代理建模框架，用于预测CO2饱和度、压力及表面位移，以支持碳封存作业的历史拟合。该框架并非通过大量昂贵的流固耦合模拟进行训练，而是结合大量低成本纯流动模拟与少量耦合模拟完成训练。纯流动模拟采用等效岩石压缩系数，经验证可为当前系统提供准确的饱和度与压力预测。针对饱和度与压力代理模型，采用循环残差U-Net架构；同时提出新型残差U-Net模型用于预测表面位移。表面位移代理模型以地质模型参数及饱和度与压力代理预测值作为输入。在多样化测试集上，所有变量的中值相对误差均低于4%。将代理模型整合至分层马尔可夫链蒙特卡洛历史拟合工作流中，并通过包含完整模型误差协方差矩阵的新处理方法纳入代理误差。考虑高度先验不确定性，地质模型以不确定地质情景参数（元参数）及相关实现为特征。仅利用原位监测井数据、仅利用表面位移数据及结合两类数据，分别对合成真实模型进行历史拟合。量化了结合两类数据所实现的增强不确定性缩减效果，并证明后验饱和度场与表面位移场与真实解高度吻合。