Conceptual framework for using system identification in reservoir production forecasting

System identification is the concept of utilizing statistical models to describe dynamic systems. System Identification modelling techniques are already popular in many science fields such as control & signal processing, process control, GPS tracking and economics. 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 for petroleum engineering purposes.
In this thesis, 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. This model can then be used to identify the reservoir for the purpose of forecasting fluid production. Only linear system identification models will be considered for identification in this study and special emphasis will be put on polynomial models. Only primary and secondary drive mechanisms will be investigated in this study.