The enhanced oil recovery (EOR) technology can be instrumental in achieving the maximum rate of return from a hydrocarbon reservoir. One of the widely implemented EOR methodologies is the cyclic steam injection (CSI) which is a thermal recovery process aiming to reduce the oil viscosity and increase the production in naturally fractured heavy-oil reservoirs. However, commercial software used for CSI modeling can be difficult to learn and implement, also can be time-consuming and costly. This paper describes three artificial neural network (ANN) based models that have been trained for accurate and fast CSI performance evaluation with easy implementation.

In this study, to model the CSI process, a commercial numerical model is used synchronously with the ANN models. Our goal is to discuss smart proxy models’ mimicking ability of the commonly used numerical models. Three ANN models have been trained with different network topologies, and transfer functions using a data set consists of 1,428 cases:

• Forward model: to predict performance indicators and viscosity contours.
• Inverse Model 1: to predict CSI design parameters.
• Inverse Model 2: to predict significant reservoir properties.

The results from the trained ANN models have been compared with the results generated by the available commercial software. It was observed that ANN models can provide the results within a few seconds while it takes more than 30 minutes in some cases for the commercial software. The computational time for the numerical model being extensive, the number of trials to be conducted to find the optimum parameters for the CSI operations can be prohibitively expensive. The trained ANN-based models are capable of providing results within a rather low error margin. The developed ANN-based models are controlled by a user-friendly graphical user interface (GUI), which decreases the time expended on learning and executing the software.

增强的石油采收率（EOR）技术有助于实现碳氢化合物储层的最大回报率。广泛实施的EOR方法之一是循环蒸汽注入（CSI），这是一种热采工艺，旨在降低油的粘度并增加天然裂缝性稠油油藏的产量。但是，用于CSI建模的商业软件可能难以学习和实施，也可能既耗时又昂贵。本文介绍了三种基于人工神经网络（ANN）的模型，这些模型已经过训练，可轻松实现准确，快速的CSI性能评估。

在这项研究中，为了对CSI过程建模，将一个商业数值模型与ANN模型同步使用。我们的目标是讨论智能代理模型对常用数值模型的模仿能力。已经使用不同的网络拓扑对三种ANN模型进行了训练，并且使用包含1,428种情况的数据集传递函数：

• 正向模型：预测性能指标和粘度轮廓。
• 逆模型1：预测CSI设计参数。
• 逆模型2：预测重要的储层性质。

经过训练的人工神经网络模型的结果已经与可用的商业软件产生的结果进行了比较。据观察，ANN模型可以在几秒钟内提供结果，而在某些情况下，对于商用软件来说，则需要30多分钟。数值模型的计算时间很长，为找到CSI操作的最佳参数而进行的试验次数可能会非常昂贵。经过训练的基于ANN的模型能够在相当低的误差范围内提供结果。已开发的基于ANN的模型由用户友好的图形用户界面（GUI）控制，从而减少了学习和执行软件所花费的时间。