Modeling the characterization of the plus fractions by using continuous distribution function

Heavy oil fluids contain large concentrations of high molecular weight components, hence large content of plus fractions. The hydrocarbon plus fractions that comprise a significant portion of naturally occurring hydrocarbon fluids cause major problems when determining the thermodynamic properties and the volumetric behavior of these fluids by equations of state. These problems arise due to the difficulty of properly characterizing the plus fractions. In order to split the plus fractions into single carbon numbers, generating the mole fraction and the respective molecular weight, a probability model that expresses the mole fraction as a continuous function of the molecular weight has been developed in this work. The developed method is based on the relationship between three parameter gamma distribution, experimental mole fraction, molecular weight and single carbon number data obtained from literature and industrial contracts. In the developed method, the three parameters of gamma distribution are obtained by the statistical regulation of gamma. The characterized mole distribution as a function of single carbon number is generated by integrating the gamma distribution between the limiting molecular weights to fit the characterized and experimental mole fractions. At least two previous carbon numbers and their mole fraction must be known to use the method. The proposed method was validated by using experimental extended fluid composition of different reservoir fluids and was compared to other splitting methods published in the literature. In addition, a worthwhile contribution of this paper is that some of the correlations which have been developed originally to predict the light oil physicochemical properties are tested for oil heavy samples to identify the ranges where they can be applied.

► A developed method improves calculation of the pseudo-components properties.
► The developed method has high accuracy comparing with the experimental data.
► The developed method proposes new approach to calculate the three parameters of the Gamma distribution.
► Some of the correlations predicting physico-chemical properties are tested to identify the rages.
► The saturation pressure generated by the developed method shows a satisfactory agreement between the predicted and the experimental data.