A Hybrid Approach for Predictive Control of Oil Well Production Using Dynamic System Identification and Real-Time Parameter Estimation

A significant part of modern natural sciences aims to establish a model-based approach to describe the behavior of physical systems and forecast their dynamics in different scenarios. The successful application of model-based analysis of transport phenomena is driven by several components, such as the consistency of a model and the uncertainty associated with its parameters. The unsatisfactory results of simulations can be caused by an improper choice of numerical methods used, but more importantly, it can be a result of wrong assumptions while establishing the model and a poor choice of closure parameters such as physical properties of fluids. This motivated the development of a hybrid approach that combines model identification directly from the data and subsequent real-time parameter estimation, which eventually minimizes the uncertainty of the developed model. This essentially brings a new model-based approach for an optimal simulation of physical phenomena by incorporating stringent interactions between all the stages of the modeling process. The identification of the governing equation from the data is achieved by a regression technique, while the model refinement is performed using the extended Kalman filter algorithm. The obtained in such a way model is then applied for control-oriented analysis. This paper discusses the deployment of such an integrated approach on a step-to-step basis and demonstrates its application to the problem of a single-phase oil inflow to the producing well.