The volumetric performance of gas condensate reservoirs is needed for designing processing facilities. A tuned Equation-of-State can be used for suitable evaluations. The required data for tuning are dew-point pressure, gas produced, liquid dropout and gas deviation factor, which are experimentally derived from Constant Volume Depletion Tests. Tuning by regression is the most common form and involves adjustment of the questionable Equation-of-State Parameters so as to obtain the best match between predicted and experimental data. Publications on tuning have not demonstrated the effect of using varying number of components and fixed weight factors to apply for best prediction.

The Peng-Robinson Equation-of-State was tuned to match Constant Volume Depletion data for several Trinidad gas-condensate samples. The optimum number of components required for best prediction was investigated in stages, starting from C7+ and using three sets of tuning parameters. The upper and lower bounds for each tuning parameter were fixed so that they remain physically realistic. The weight factors assigned to each experimental data set were kept constant for every sample. Apart from liquid drop out, excellent predictions were obtained when Binary Interaction Coefficient between methane and the heavy carbon groups were tuned to match dew point pressure. By including the parameters of Oa and Ob for methane and Oa and Ob for the last carbon group excellent prediction of liquid drop out was attained.

This paper demonstrated that an optimum number of components are required for best prediction of Constant Volume Depletion data, with errors less than 10 %. The required number was different for each sample studied and varied from a minimum of C15+ to a maximum of C25+. A higher number can reduce this accuracy of prediction. Some consistency in tuning, with regards to tuning parameters and weight factors was also demonstrated and resulted in less time and effort required. This approach can be equally applied to other gas condensate samples.

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