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.