K-value program for crude oil components at high pressures based on PVT laboratory data and genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "K. A. Fattah",
  title =        "K-value program for crude oil components at high
                 pressures based on PVT laboratory data and genetic
  journal =      "Journal of King Saud University - Engineering
  volume =       "24",
  number =       "2",
  pages =        "141--149",
  year =         "2012",
  ISSN =         "1018-3639",
  DOI =          "doi:10.1016/j.jksues.2011.06.002",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1018363911000584",
  keywords =     "genetic algorithms, genetic programming, K-value,
                 Correlation, Genetic program, PVT lab report, Crude
                 oil, High pressures",
  abstract =     "Equilibrium ratios play a fundamental role in
                 understanding the phase behaviour of hydrocarbon
                 mixtures. They are important in predicting
                 compositional changes under varying temperatures and
                 pressures in the reservoirs, surface separators, and
                 production and transportation facilities. In
                 particular, they are critical for reliable and
                 successful compositional reservoir simulation. Several
                 techniques are available in the literature to estimate
                 the K-values. This paper presents a new model for
                 predicting K values with genetic programming (GP). The
                 new model is applied to multicomponent mixtures. In
                 this paper, 732 high-pressure K-values obtained from
                 PVT analysis of 17 crude oil and gas samples from a
                 number of petroleum reservoirs in Arabian Gulf are
                 used. Constant Volume Depletion (CVD) and Differential
                 Liberation (DL) were conducted for these samples.
                 Material balance techniques were used to extract the
                 K-values of crude oil and gas components from the
                 constant volume depletion and differential liberation
                 tests for the oil and gas samples, respectively. These
                 K-values were then used to build the model using the
                 Discipulus software, a commercial Genetic Programming
                 system, and the results of K-values were compared with
                 the values obtained from published correlations.
                 Comparisons of results show that the currently
                 published correlations give poor estimates of K-values
                 for all components, while the proposed new model
                 improved significantly the average absolute deviation
                 error for all components. The average absolute error
                 between experimental and predicted K-values for the new
                 model was 4.355percent compared to 20.5percent for the
                 Almehaideb correlation, 76.1percent for the Whitson and
                 Torp correlation, 84.27percent for the Wilson
                 correlation, and 105.8 for the McWilliams

Genetic Programming entries for Khaled Abdel Fattah Elshreef