Recent experience in applying recurrent neural networks (RNNs) to interpreting permanent downhole gauge records has highlighted the potential utility of machine learning algorithms to learn reservoir behavior from data. The power of the RNN resides in its ability to retain information in a form of memory of previous patterns and information contained in the previous behavior of phenomena being modeled. This memory plays a role of informing the decision at the present time by using what happened in the past. This property suggests the RNN as a suitable choice to model sequences of reservoir information, even when the reservoir modeler is faced with incomplete knowledge of the underlying physical system.

Convolutional neural networks (CNNs) are another variant of the machine learning algorithm that have shown promise in sequence modeling domains, such as audio synthesis and machine translation. In this study, RNNs and CNNs were applied to tasks that traditionally would be modeled by a reservoir simulator. This was achieved by formulating the relationship between physical quantities of interest from subsurface reservoirs as a sequence mapping problem. In addition, the performance of a CNN layer as compared with an RNN was evaluated systematically to investigate their capabilities in a variety of tasks of interest to the reservoir engineer.

Preliminary results suggest that CNNs, with specific design modifications, are as capable as RNNs in modeling sequences of information, and as reliable when making inferences to cases that have not been seen by the algorithm during training. Design details and reasons pertaining to the way these two seemingly different architectures process information and handle memory are also discussed.

应用递归神经网络（RNN）解释永久性井下仪器记录的最新经验强调了机器学习算法从数据中学习储层行为的潜在效用。RNN的功能在于以信息的形式保留先前模式的信息以及包含在正在建模的现象的先前行为中的信息的能力。该记忆起着利用过去发生的事情通知当前决策的作用。此属性建议使用RNN作为建模储层信息序列的合适选择，即使储层建模人员面临对基础物理系统的不完全了解也是如此。

卷积神经网络（CNN）是机器学习算法的另一种变体，它在序列建模领域（如音频合成和机器翻译）中显示出了希望。在这项研究中，RNN和CNN被应用于传统上由油藏模拟器建模的任务。这是通过将地下储层中感兴趣的物理量之间的关系公式化为序列映射问题来实现的。另外，系统地评估了CNN层与RNN的性能，以研究其在储层工程师感兴趣的各种任务中的能力。

初步结果表明，经过特定设计修改后的CNN在信息序列建模中的能力与RNN一样，并且在对算法在训练过程中未发现的情况进行推断时同样可靠。还讨论了与这两个看似不同的体系结构处理信息和处理内存的方式有关的设计细节和原因。