Improvement of multiphase flow rate model for chokes

Improvement of multiphase flow rate model for chokes This paper evaluates three models for predicting the flow rate of a multiphase mixture through a choke that is commonly used in the oil and gas industry. The paper also presents the development and evaluation of a new and more accurate choke model based on one of the existing models. Evaluation of these models was done by using experimental data published by Schüller (2003, 2006). The three choke models selected for benchmarking were: Sachdeva et al. (1986), Perkins (1993), Al-Safran and Kelkar (2007).From the evaluation performed, the model by Sachdeva et al. (1986) was found to have a mean relative error of 1.76%, a mean absolute error of 10.52% and a standard deviation of 12.49%. The Perkins (1993) model gave a mean relative error of −24.17%, a mean absolute error of 30.74% and a standard deviation of 25.91%. The Al-Safran and Kelkar (2007) model exhibited a mean relative error of −10.10%, a mean absolute error of 17.48% and a standard deviation of 17.6%. All models both underpredict and overpredict the experimental data.Based on the evaluation results, the Sachdeva model was found to be the best model for predicting mass flow rate through restrictions. The Sachdeva model was therefore modified and improved by introducing a slippage factor. Error analysis of the modified Sachdeva model showed a mean relative error of −0.4%, a mean absolute error of 6.12% and a standard deviation of 7.66%. The modified Sachdeva model was calibrated by changing the discharge coefficient (CD) and the best value of CD for model calibration was found to be 0.65.The model improves the predictability considerably by reducing the deviation from the original Sachdeva model by half and all the predictions seem to be within the range of 10% accuracy. However, it is recommended to evaluate the new model using more experimental data. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Petroleum Science and Engineering Elsevier

Improvement of multiphase flow rate model for chokes

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Publisher
Elsevier
Copyright
Copyright © 2016 Elsevier B.V.
ISSN
0920-4105
eISSN
1873-4715
D.O.I.
10.1016/j.petrol.2016.05.022
Publisher site
See Article on Publisher Site

Abstract

This paper evaluates three models for predicting the flow rate of a multiphase mixture through a choke that is commonly used in the oil and gas industry. The paper also presents the development and evaluation of a new and more accurate choke model based on one of the existing models. Evaluation of these models was done by using experimental data published by Schüller (2003, 2006). The three choke models selected for benchmarking were: Sachdeva et al. (1986), Perkins (1993), Al-Safran and Kelkar (2007).From the evaluation performed, the model by Sachdeva et al. (1986) was found to have a mean relative error of 1.76%, a mean absolute error of 10.52% and a standard deviation of 12.49%. The Perkins (1993) model gave a mean relative error of −24.17%, a mean absolute error of 30.74% and a standard deviation of 25.91%. The Al-Safran and Kelkar (2007) model exhibited a mean relative error of −10.10%, a mean absolute error of 17.48% and a standard deviation of 17.6%. All models both underpredict and overpredict the experimental data.Based on the evaluation results, the Sachdeva model was found to be the best model for predicting mass flow rate through restrictions. The Sachdeva model was therefore modified and improved by introducing a slippage factor. Error analysis of the modified Sachdeva model showed a mean relative error of −0.4%, a mean absolute error of 6.12% and a standard deviation of 7.66%. The modified Sachdeva model was calibrated by changing the discharge coefficient (CD) and the best value of CD for model calibration was found to be 0.65.The model improves the predictability considerably by reducing the deviation from the original Sachdeva model by half and all the predictions seem to be within the range of 10% accuracy. However, it is recommended to evaluate the new model using more experimental data.

Journal

Journal of Petroleum Science and EngineeringElsevier

Published: Sep 1, 2016

References

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