By a News Reporter-Staff News Editor at Energy Weekly News -- Investigators discuss new findings in Support Vector Machines. According to news reporting originating from Ahwaz, Iran, by VerticalNews correspondents, research stated, "Estimation of the viscosity of naturally occurring petroleum gases is essential to provide more accurate analysis of gas reservoir engineering problems. In this study, a new soft computing approach, namely, least square support vector machine (LSSVM) modeling, optimized with a coupled simulated annealing technique was applied for estimation of the natural gas viscosities at different temperature and pressure conditions."
Our news editors obtained a quote from the research from the Petroleum University of Technology, "This model was developed based on 2485 viscosity data sets of 22 gas mixtures. The model predictions showed an average absolute relative error of 0.26% and a correlation coefficient of 0.99. The results of the proposed model were also compared with the well-known predictive models/correlations available in the literature. It has been observed that the proposed model correctly captures the physical trend of changing the natural gas viscosity as a function of the temperature and pressure. Finally, sensitivity analysis was performed to assess the effect of the gas viscosity uncertainty on the cumulative gas production for a synthetic natural gas reservoir, using a numerical reservoir simulation."
According to the news editors, the research concluded: "Results revealed that applications of LSSVM modeling can lead to a more accurate and reliable estimation of the gas viscosity over a wide range of reservoir conditions."
For more information on this research see: State-of-the-Art Least Square Support Vector Machine Application for Accurate Determination of Natural Gas Viscosity. Industrial & Engineering Chemistry Research, 2014;53(2):945-958. Industrial & Engineering Chemistry Research can be contacted at: Amer Chemical Soc, 1155 16TH St, NW, Washington, DC 20036, USA. (American Chemical Society - www.acs.org; Industrial & Engineering Chemistry Research - www.pubs.acs.org/journal/iecred)
The news editors report that additional information may be obtained by contacting A. Fayazi, Petr Univ Technol, Dept. of Petr Engn, Ahwaz, Iran. Additional authors for this research include M. Arabloo, A. Shokrollahi, M.H. Zargari and M.H. Ghazanfari.
Keywords for this news article include: Iran, Asia, Ahwaz, Energy, Oil & Gas, Natural Gas, Machine Learning, Emerging Technologies, Support Vector Machines
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