Defining a reliable forecasting model in petroleum reservoir management has always been a challenge. In cases where reservoir description is limited and when fast decision with an acceptable accuracy is required, current methods have significant limitations and restrictions. System identification, which is based on historical data and statistical methods could be promising. However, the complexity of a petroleum reservoir system and the availability of numerous model structures in system identification make it challenging to adapt this method effectively. In this paper, a conceptual framework for using system identification is proposed. Based on a reservoir's recovery mechanism, the conceptual framework will help to systematically select an appropriate model structure from the various model structures available in system identification. The results show that system identification polynomial models can provide very accurate models, in a very short time, to predict performance of reservoirs under primary and secondary recovery mechanisms. These models have also the potential to be established as a practical, cost-effective and robust tool for forecasting reservoir fluid production. © 2016 The Authors.