Improved predictions of wellhead choke liquid critical-flow rates: Modelling based on hybrid neural network training learning based optimization
Authors:
- Abouzar ChoubinehACAbouzar Choubineh
- Hamzeh GhorbaniHGHamzeh Ghorbani
- David A. WoodDWDavid A. Wood
- Seyedeh Robab MoosaviSMSeyedeh Robab Moosavi
- Elias KhalafiEKElias Khalafi
- Erfan SadatshojaeiESErfan Sadatshojaei
Abstract
No abstract availableFunding Information
- NISOC
This publication has 48 references indexed in Scilit:
- Estimation of the CO2-oil minimum miscibility pressure for enhanced oil recoveryPetroleum Science and Technology, 2016
- Artificial Neural Network (ANN) to estimate reservoir parameters in Naturally Fractured Reservoirs using well test dataJournal of Petroleum Science and Engineering, 2014
- The genetic algorithm based back propagation neural network for MMP prediction in CO2-EOR processFuel, 2014
- A note on teaching–learning-based optimization algorithmInformation Sciences, 2012
- New multiphase choke correlations for a high flow rate Iranian oil fieldMechanical Sciences, 2012
- Structural optimization and experimental investigation of supersonic ejectors for boosting low pressure natural gasApplied Thermal Engineering, 2009
- New Correlations for Critical and Subcritical Two-Phase Flow through Surface Chokes in High-Rate Oil Wells2009
- Performance of wellhead chokes during sub-critical flow of gas condensatesJournal of Petroleum Science and Engineering, 2008
- Artificial neural network model for synthetic streamflow generationWater Resources Management, 2006
- Critical Flashing Flows in Nozzles With Subcooled Inlet ConditionsJournal of Heat Transfer, 1983