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.

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