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Information Seminar Paper

Title

SOFT COMPUTING MODEL FOR PREDICTION OF SOUR AND NATURAL GAS VISCOSITY

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Abstract

 PURE HYDROCARBONS AND NATURAL GASES VISCOSITY ARE ESSENTIAL FOR RELIABLE EXPLOITATION, RESERVOIRS CHARACTERIZATION AND SIMULATION, TRANSPORTATION AND OPTIMUM CONSUMPTION. THE MOST COMMON SOURCES OF PURE HYDROCARBONS AND NATURAL GAS VISCOSITY VALUES ARE LABORATORY EXPERIMENTS AND EMPIRICAL CORRELATIONS. NEW TOOLS ARE NEEDED WHEN THERE IS NO AVAILABLE EXPERIMENTAL DATA FOR THE REQUIRED COMPOSITION, PRESSURE, AND TEMPERATURE CONDITIONS. WHEN THERE IS NO AVAILABLE EXPERIMENTAL DATA FOR THE REQUIRED COMPOSITION, PRESSURE, AND TEMPERATURE CONDITIONS, THE USE OF PREDICTIVE METHODS BECOMES IMPORTANT. IN THIS WORK, A NEW RELIABLE ARTIFICIAL INTELLIGENCE MODEL BASED ON FEED FORWARD ARTIFICIAL NEURAL NETWORK IS PROPOSED FOR THE PREDICTION OF VISCOSITY OF PURE HYDROCARBONS AS WELL AS GAS MIXTURES CONTAINING HEAVY HYDROCARBON COMPONENTS AND IMPURITIES SUCH AS CARBON DIOXIDE, NITROGEN, HELIUM, AND HYDROCARBON SULFIDE USING OVER 3800 DATA SETS. MOREOVER, COMPARATIVE STUDIES BETWEEN THE THIS MODEL AND EXISTING CORRELATIONS ARE PERFORMED AND STATISTICAL AND GRAPHICAL ERROR ANALYSES ARE PRESENTED. THE OBTAINED RESULTS ILLUSTRATE THAT THE PROPOSED MODEL IS MORE ROBUST, RELIABLE AND CONSISTENT THAN THE EXISTING CORRELATIONS FOR THE PREDICTION OF PURE AND NATURAL GAS VISCOSITY.

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