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

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@Article{Fattah2012141,

• author = "K. A. Fattah",
• title = "K-value program for crude oil components at high pressures based on PVT laboratory data and genetic programming",
• journal = "Journal of King Saud University - Engineering Sciences",
• 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 correlation.",

}

Genetic Programming entries for Khaled Abdel Fattah Elshreef

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