A Bayesian approach to the dynamic modeling of ESP-lifted oil well systems: An experimental validation on an ESP prototype

• Published in: Journal of petroleum science & engineering 2021-10, Vol.205, p.108880, Article 108880
• Main Authors: Costa, E.A., Abreu, O.S.L. de, Silva, T.de O., Ribeiro, M.P., Schnitman, L.
• Format: Article
• Language: English
• Subjects: Bayesian inference; Electrical submersible pump; Modeling
• ISSN: 0920-4105; 1873-4715
• DOI: 10.1016/j.petrol.2021.108880

This article presents an integrated method for estimating parameters for an electric submersible pump system with process variables data. Validation of a phenomenological model is also performed. The parameters and the associated probability density function are obtained through Bayesian inference, and the model validation is achieved in two stages. The first one is the validation of the dynamic response in which the model is compared with the experimental data. The second is achieved by comparing the regions covered by the experimental data and the model, both in steady-state. The experimental data's uncertainty is assessed using the Guide for the Expression of Uncertainty in Measurement. In turn, the uncertainty of the model's prediction is obtained by propagating the probability density function parameters. The results indicate that the method can provide a model to represent the system behavior within the existing uncertainties. Additionally, the procedure can be applied in oil production fields to provide substitute models for general purposes, such as production control, optimization, and assistance. •Comparison of model prediction and experimental data with uncertainty assessment.•Validated model to represent the behavior of the experimental apparatus.•Data from sensors of the plant to update the equipment's operating parameters.•The model is a candidate to be used as a surrogate model for the real plant.