Development of a generalized model for predicting the composition of homologous groups derived from molecular type analyses to characterize petroleum fractions

Two best-known molecular type analyses are PNA and SAP methods, which divide an olefin-free fraction into the sub-fractions (Paraffins, Naphthenes, and Aromatics) and (Saturates, Aromatics, and Poly-nuclear aromatics), respectively. In this study, a new generalized model has been put forward for predicting both PNA and SAP content of petroleum fractions in terms of their measurable bulk properties. The model receives the normal boiling point (Tb), specific gravity (SG) and refractive index (RI) as input parameters to predict PNA and SAP compositions of petroleum fractions. Furthermore, two auxiliary relations are developed for the estimation of the refractive index (based on SG andTb) and normal boiling point (based onMw and SG), to be used in situations that some of the input data are unavailable. Auxiliary correlations will be able to enhance the model flexibility so that it can predict the PNA or SAP composition of petroleum fractions, applying either the pair inputs (Tb,SG) or (Mw, SG). The model validation was entirely checked against a wide range of experimental data available in the literature, and good conformity was observed. The mean of AADs in the prediction of both PNA and SAP compositions of 156 light and heavy petroleum cuts revealed the value of 2.5% for the proposed model. A careful comparison was also performed between the proposed model and other existing well-known methods. The evaluation of results showed an AAD of 7.8% for the Riazi-Daubert method and an AAD of 9.5% for the Van Nes and Van Westen method. Moreover, sensitivity analysis of the model outputs was carefully surveyed with respect to its input parameters. It was viewed that the model outputs were less affected by probable errors that occurred in input parameters rather than the other methods. Given that the proposed model converts each petroleum cut to a well-defined ternary mixture of PNA or SAP sub-fractions, it can significantly be efficient in the characterization of reservoir fluids. Based on this, a comprehensive evaluation was conducted to verify the proposed model influences on predicting bubble pressures of 20 oil samples and simulating the differential liberation test for three oil samples. The outcome of the evaluations indicated that the proposed model could effectively improve the oil characterization process.

Automated summary

Two best-known molecular type analyses, PNA and SAP methods, have been generalized into a new model for predicting PNA and SAP content of petroleum fractions using input parameters such as Tb, SG, and RI. The model was validated against experimental data and showed good conformity, with an AAD of 2.5% for predicting PNA and SAP compositions. Furthermore, the model’s sensitivity analysis demonstrated that it is less affected by input errors compared to other existing methods, highlighting its efficiency for characterizing reservoir fluids.