A Comprehensive Study on the Current Pressure Drop Calculation in Multiphase Vertical Wells; Current Trends and Future Prospective


	Journal of Applied Sciences
	Year: 2014 \| Volume: 14 \| Issue: 23 \| Page No.: 3162-3171 DOI: 10.3923/jas.2014.3162.3171

A Comprehensive Study on the Current Pressure Drop Calculation in Multiphase Vertical Wells; Current Trends and Future Prospective

Musaab M. Ahmed and Mohammed A. Ayoub

Abstract: A reliable estimation of the pressure drop in well tubing is essential for the solution of a number of important production engineering and reservoir analysis problems. Many empirical correlation and mechanistic models have been proposed to estimate the pressure drop in vertical wells that produce a mixture of oil, water and gas. Although, many correlations and models are available to calculate the pressure loss, these models developed based on certain assumption and for particular range of data where it may not be applicable to be used in different sets of data. This study presents an investigation on the predictive performance evaluation for the reliable methods used to calculate the pressure drop in multiphase vertical wells taking into consideration the dimensions of each model. Most correlations and models created to calculate pressure drop were developed based on accurately and reliably measured flow parameters. However, it can only work best on the proposed data range. Statistical error analysis and graphical error analysis are used to analyze the variation between predicted values and actual ones. Hence, it showed most reliable methods that can perform well in different well conditions. Based on the analysis of this study, the artificial neural network models had showed better prediction accuracy and minimum number of variables even if other data beyond the range of data is used.

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How to cite this article:

Musaab M. Ahmed and Mohammed A. Ayoub, 2014. A Comprehensive Study on the Current Pressure Drop Calculation in Multiphase Vertical Wells; Current Trends and Future Prospective. Journal of Applied Sciences, 14: 3162-3171.

DOI: 10.3923/jas.2014.3162.3171

URL: https://scialert.net/abstract/?doi=jas.2014.3162.3171

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