Gas–oil ratio correlation (R s ) for gas condensate using genetic programming

A new correlation for solution gas–oil ratio ( R s ) for gas condensate reservoir was developed in this paper by using genetic programming algorithm of a commercial software (Discipulus) program. Matching PVT experimental data with an equation of state model, a commercial simulator (Eclipse simulator) was used to calculate the solution gas–oil ratio ( R s ) values used in this study. More than 1,800 solution gas–oil ratio ( R s ) values obtained from the analysis of eight gas condensate fluid PVT laboratory reports, selected under a wide range of reservoir temperature and pressure, composition and condensate yield, were used. Comparisons of the results showed that currently published correlations of gas–oil ratio ( R s ) for gas condensate gave poor estimates of its value (the average absolute error for Standing correlation was 63.48 with a standard deviation (SD) equal to 0.724, the average absolute error for Glaso correlation was 61.19 % with a SD equal to 0.688, the average absolute error for Vasques and Beggs correlation was 52.22 % with a SD equal to 0.512, the average absolute error for Marhoun correlation was 56.34 % with a SD equal to 0.519 and the average absolute error for Fattah et al. correlation was 18.6 % with a SD equal to 0.049). The proposed new correlation improved extensively the average absolute error for gas condensate fluids. The average absolute error for the new correlation was 10.54 % with a SD equal to 0.035. Also, the hit-rate ( R 2 ) of the new correlation was 0.9799 and the fitness variance was 0.012. The importance of the new correlation comes from depending only on readily available production data in the field and can have wide applications when representative PVT lab reports are not available.