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• W3094997652 endingPage "108665" @default.
• W3094997652 startingPage "108665" @default.
• W3094997652 abstract "The accurate prediction of pressure has extensive applications in the petroleum industry, especially in the optimization of continuous field production, quantifying reservoir performance, adjusting the cost of oil production and assessment of workover operations. On the other hand, the complexity associated with the two-phase flow system makes the computational determination of bottom-hole pressure difficult and time-consuming, so in the current work, two novel machine learning approaches based on Gradient tree boosting (GTB) and Extreme Learning Machine (ELM) have been proposed. The comparison of 458 actual pressure values and proposed GTB and ELM outputs has clarified that machine learning approaches have excellent performance in oil field calculations with R-squared values of 1 and 0.999 and also mean relative errors lower than 4% for these models. Additionally, sensitivity analysis on the input variables shows that the most effective parameter in the determination of bottom-hole pressure is well-head pressure." @default.
• W3094997652 created "2020-11-09" @default.
• W3094997652 creator A5013097925 @default.
• W3094997652 creator A5072047674 @default.
• W3094997652 date "2021-03-01" @default.
• W3094997652 modified "2023-09-27" @default.
• W3094997652 title "Prediction of pressure in different two-phase flow conditions: Machine learning applications" @default.
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• W3094997652 doi "https://doi.org/10.1016/j.measurement.2020.108665" @default.
• W3094997652 hasPublicationYear "2021" @default.
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