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 mean and standard deviation of
log-permeability, permeability anisotropy ratio, horizontal correlation length,
etc. 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 Markov chain
Monte Carlo history matching workflow, where the goal is to generate history
matched 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.

在这项研究中，我们利用基于深度学习的代理模型来处理具有高度地质不确定性的地质碳储存系统，通过使用大量地质情景下的地质模型实现了准确的预测，并将其用于马尔可夫链蒙特卡洛历史匹配工作流程以降低地质不确定性，从而得到与真实模型更接近的 3D 压力和饱和度。