A Modified Model for Prediction of Gas Viscosities of Yemeni Gas Fields

Authors

  • Khaled Saeed Ba-Jaalah Department of Petroleum Engineering, Faculty of Engineering & Petroleum, Hadhramout University. Received on 4/12/2017 and Accepted for Publication on19/11/2018
  • Abbas Mohamed Al-Khudafi Department of Petroleum Engineering, Faculty of Engineering & Petroleum, Hadhramout University. Received on 4/12/2017 and Accepted for Publication on19/11/2018
  • Amer Badr BinMerdhah Department of Petroleum Engineering, Faculty of Engineering & Petroleum, Hadhramout University. Received on 4/12/2017 and Accepted for Publication on19/11/2018

Keywords:

Models, Natural gas, Viscosity, Empirical Correlation, Yemeni gas

Abstract

Gas viscosity is an important physical property that controls and influences the flow of gas through porous media and
pipe networks. An accurate gas viscosity model is essential for use with reservoir and process simulators. The present
study is concerned with the evaluation of available gas viscosity correlations. The paper also presents new models for
predicting gas viscosity for Yemeni gas reservoir. The most often used different correlations for gas viscosity were
assist for Yemeni gas samples. The limitations of these correlations have been analyzed using Yemeni field data.
Calculations are made using 120 experimental data points from Yemeni gas reservoirs. The most common gas
viscosity correlations such as Lee et al., Carr et al. and Dean and Stile were selected as a suitable compositional
viscosity model for developing new models using fitting the Yemeni gas reservoirs data. Initially, all considered
models in this study were evaluated for the Yemeni gas data. To improve the performance of these correlations their
coefficients were regenerated to fit the Yemeni gas samples data set using linear and nonlinear regression methods.
The behavior of new developed models was evaluated using statistical error analysis. The new developed models
show accuracy with the desirable engineering limits

Downloads

Published

2023-11-23