This paper demonstrates a deep neural network approach for predicting carbon dioxide (CO₂) plume migration from an injection well in heterogeneous formations with high computational efficiency. With the data generation and training procedures proposed in this paper, we show that the deep neural network model can generate predictions of CO₂ plume migration that are as accurate as traditional numerical simulation, given input variables of a permeability field, an injection duration, injection rate, and injection location. The neural network model can deal with permeability fields that have high degrees of heterogeneity. Unlike previous studies which did not consider the effect of buoyancy, here we also show that the neural network model can learn the consequences of the interplay of gravity, viscous, and capillary forces, which is critically important for predicting CO₂ plume migration. The neural network model has an excellent ability to generalize within the training data ranges and to a limited extent, the ability to extrapolate beyond the training data ranges. To improve the prediction accuracy when the neural network model needs to extrapolate to situations or parameters not contained in the training set, we propose a transfer learning (fine-tuning) procedure that can quickly teach the trained neural network model new information without going through massive data collection and retraining. With the approaches described in this paper, we have demonstrated many of the building blocks required for developing a general-purpose neural network for predicting CO₂ plume migration away from an injection well.

本文演示了一种用于预测二氧化碳（CO ₂）羽流从非均质地层中的注入井中迁移的深度神经网络方法，具有很高的计算效率。通过本文提出的数据生成和训练过程，我们证明了深度神经网络模型可以生成CO _2的预测在给定渗透率场，注入持续时间，注入速率和注入位置的输入变量的情况下，羽流的迁移与传统数值模拟一样精确。神经网络模型可以处理具有高度异质性的渗透率场。与以前没有考虑浮力影响的研究不同，我们在这里还显示了神经网络模型可以学习重力，粘性和毛细作用力相互作用的结果，这对于预测CO ₂至关重要羽状迁移。神经网络模型具有出色的概括能力，可以在训练数据范围内进行归纳，并且在一定程度上可以推断超出训练数据范围的能力。为了在神经网络模型需要外推到训练集中未包含的情况或参数时提高预测准确性，我们提出了一种转移学习（微调）程序，该程序可以快速传授训练后的神经网络模型新信息，而无需花费大量精力。数据收集和再培训。通过本文中描述的方法，我们已经证明了开发通用神经网络以预测CO ₂羽流从注入井迁移所需要的许多构造块。