On Uncertainty Analysis of the Rate Controlled Production (RCP) Model


  • Soheila Taghavi
  • Ali Ghaderi




AICV performance, RCP model, Bayesian inference, parameter estimation, MCMC, Stan


Rate controlled production (RCP) model is used to simulate and investigate the performance of the oil wells which are completed by autonomous inflow control devices. In order to quantify the performance of the RCP model, a dimensionless version of the model is considered, and its parameters are estimated. We demonstrate how the model and the measurement uncertainties can be quantified within the Bayesian statistical inference framework. In this relation, Hamilton Monte Carlo (HMC) is used to draw samples from the joint posterior probability distribution. We demonstrate that at the calibration step the modified model is able to capture the variations in the measurements. However, the cross- validation with the new data has revealed that the modified model tends to overpredict the pressure drop. This inadequacy cannot be explained by the measurement noise or the uncertainty in the estimated parameters. These results also imply that the original RCP model needs revision.


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