Prediction of Dew Point Pressure in Gas Condensate Reservoirs Based On a Combination of Gene Expression Programming (GEP) and Multiple Regression Analysis

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Gas condensate reservoirs represent unique and clean hydrocarbon source of energy, so prediction of their thermodynamic criteria especially dewpoint pressure (Pd) is crucial for reservoir characterization and management, since declining of initial reservoir pressure below dewpoint pressure result in liquid built up near wellbore and reduce gas productivity index. In this study, a mathematical modeling developed to estimate dewpoint pressure at reservoir temperature using reliable, precise, well-organized gene expression programming (GEP) approach in combination with multiple non-linear regression analysis. The dataset comprises 453 published data points, and the model developed as a function of compositional analysis of hydrocarbons components (ZC1-ZC7+), physical properties of heptane plus fractions (C7 +) including molecular weight and specific gravity, the mole fraction of nonhydrocarbons (ZCO2& ZN2) and reservoir temperature. Experimental Pressure-Volume-Temperature (PVT) analysis including constant composition expansion (CCE) at reservoir conditions and compositional analysis are carried out through 27 gas condensate samples not used in model development, and covering a great range of PVT properties to evaluate the new predictive model accuracy. Assessment and validation of the developed and published correlations carried out by a statistical and graphical error analyses. The obtained relative errors indicate that the developed model employed as an alternative approach monitoring the dewpoint pressure of gas condensate reservoirs when the required real data are not accessible.