Compressibility factor model of sweet, sour, and condensate gases using genetic programming

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  author =       "Eissa M. El-M. Shokir and Musaed N. El-Awad and 
                 Adulhrahman A. Al-Quraishi and Osama A. Al-Mahdy",
  title =        "Compressibility factor model of sweet, sour, and
                 condensate gases using genetic programming",
  journal =      "Chemical Engineering Research and Design",
  volume =       "90",
  number =       "6",
  pages =        "785--792",
  year =         "2012",
  note =         "Special Issue on the 3rd European Process
                 intensification Conference",
  ISSN =         "0263-8762",
  DOI =          "doi:10.1016/j.cherd.2011.10.006",
  URL =          "",
  keywords =     "genetic algorithms, genetic programming, Gas
                 compressibility factor, Sour gas, Condensate gas",
  abstract =     "Gas compressibility factor (z-factor) is necessary in
                 most petroleum engineering calculations. The most
                 common sources of z-factor values are experimental
                 measurements, equations of state (EOS) and empirical
                 correlations. There are more than twenty correlations
                 available with two variables for calculating the
                 z-factor from fitting Standing-Katz chart values in an
                 EOS or just through fitting techniques. However, these
                 correlations are too complex, which require initial
                 value and longer computations, and have significant
                 error. This work presents a new model for estimating
                 z-factors of sweet gases, sour gases and gas
                 condensates using genetic programming (GP). The
                 z-factor model was developed using pseudo-reduced
                 pressure, and pseudo-reduced temperature. Moreover, two
                 new models of pseudo-critical pressure and temperature
                 were built as a function of the gas composition (mol
                 percent of C1-C7+, H2S, CO2, and N2) and the specific
                 gravity of the C7+. The developed new GP-based model
                 yields a more accurate prediction of gas z-factor
                 compared to the commonly used correlations and EOS's.",

Genetic Programming entries for Eissa M El-M Shokir Musaed N El-Awad Adulhrahman A Al-Quraishi Osama A Al-Mahdy