27 citations as recorded by crossref.

1. Prediction of gas flow rates from gas condensate reservoirs through wellhead chokes using a firefly optimization algorithm H. Ghorbani et al. https://doi.org/10.1016/j.jngse.2017.04.034
2. New universal two-phase choke correlations developed using non-linear multivariable optimization technique M. Alrumah & R. Alenezi https://doi.org/10.1016/S2307-1877(25)00624-8
3. Predicting liquid flow-rate performance through wellhead chokes with genetic and solver optimizers: an oil field case study H. Ghorbani et al. https://doi.org/10.1007/s13202-018-0532-6
4. Prediction performance advantages of deep machine learning algorithms for two-phase flow rates through wellhead chokes H. Barjouei et al. https://doi.org/10.1007/s13202-021-01087-4
5. Machine Learning Applications to Predict Surface Oil Rates for High Gas Oil Ratio Reservoirs A. Ibrahim et al. https://doi.org/10.1115/1.4052485
6. An Investigation into Sub-Critical Choke Flow Performance in High Rate Gas Condensate Wells H. Nasriani et al. https://doi.org/10.3390/en12203992
7. Prediction of Surface Oil Rates for Volatile Oil and Gas Condensate Reservoirs Using Artificial Intelligence Techniques R. Al Dhaif et al. https://doi.org/10.1115/1.4051298
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9. Piecewise Gilbert-type correlation for two-phase flowback through wellhead chokes in hydraulically fractured shale gas wells Y. Jiang et al. https://doi.org/10.1080/10916466.2022.2120500
10. Simplified Neural Network-Based Models for Oil Flow Rate Prediction U. Umana et al. https://doi.org/10.11648/j.pse.20240802.12
11. Oil and gas well rate estimation by choke formula: semi-analytical approach M. Kargarpour https://doi.org/10.1007/s13202-019-0629-6
12. Prediction of oil rates using Machine Learning for high gas oil ratio and water cut reservoirs R. Al Dhaif et al. https://doi.org/10.1016/j.flowmeasinst.2021.102065
13. Experimental analysis and model evaluation of gas-liquid two phase flow through choke in a vertical tube C. Xie et al. https://doi.org/10.1016/j.petrol.2021.109902
14. A novel comprehensive model for predicting production of downhole choke wells C. Xie et al. https://doi.org/10.1016/j.fuel.2021.122944
15. Prediction of well performance parameters for wellhead choke flow optimization using MLP-GA A. Akbari & M. Rahimi https://doi.org/10.1080/12269328.2026.2613005
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17. Application of Artificial Intelligence to Estimate Oil Flow Rate in Gas-Lift Wells M. Khan et al. https://doi.org/10.1007/s11053-020-09675-7
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19. Prediction of gas-liquid two-phase choke flow using Gaussian process regression Y. Jiang et al. https://doi.org/10.1016/j.flowmeasinst.2021.102044
20. Improved Multiphase Flow Rate Models for Chokes in the Algerian HMD Oil Field N. Tellache et al. https://doi.org/10.1007/s13369-020-04971-z
21. Applications of Artificial Intelligence to Predict Oil Rate for High Gas–Oil Ratio and Water-Cut Wells A. Ibrahim et al. https://doi.org/10.1021/acsomega.1c01676
22. Modelling oil and gas flow rate through chokes: A critical review of extant models O. Agwu et al. https://doi.org/10.1016/j.petrol.2021.109775
23. Workflow to predict wellhead choke performance during multiphase flow using machine learning S. Alarifi https://doi.org/10.1016/j.petrol.2022.110563
24. Machine learning-based prediction of well performance parameters for wellhead choke flow optimization A. Akbari et al. https://doi.org/10.1038/s41598-025-27851-8
25. New generalized correlations for oil rate predictions through wellhead chokes for high GOR reservoirs A. Ibrahim et al. https://doi.org/10.1007/s12517-022-10387-w
26. A new correlation for prediction of sub-critical two-phase flow pressure drop through large-sized wellhead chokes S. Seidi & T. Sayahi https://doi.org/10.1016/j.jngse.2015.06.025
27. Adaptive neuro-fuzzy algorithm applied to predict and control multi-phase flow rates through wellhead chokes H. Ghorbani et al. https://doi.org/10.1016/j.flowmeasinst.2020.101849