PIONA Characterization Matrix

When simulation models involve heavy hydrocarbon feeds the use of individual components to represent the exact composition of the mixture is not realistic due to the presence of potentially thousands of individual chemical species, corresponding restrictions on currently available analytical techniques, and inefficient use of computer resources for simulation.




Techniques for modeling the material based on the use of pseudo components are applied and are often based on an easily measured property like the normal boiling point. In these situations one pseudo-component with a given average normal boiling point represents a mixture of many pure compounds that happen to boil within a certain temperature range. It is important to note that this average component represents not only components boiling at different temperatures, but also components that have distinct chemical characteristics; like aromatics, naphthenes and paraffins with different individual properties such as density and viscosity.

The lumped component or pseudo-component grouping method works well in situations where volatility is the major property of interest and no chemical reactions are present. However, the pseudo-component modeling technique fails whenever chemically driven separations or chemical reactors are involved in a simulation. The cause of these complications is the lack of chemical information in the pseudo-components.

In this section, VMGSim introduces a different and new approach of the pseudo-component characterization technique: the PIONA (n-Paraffin, Iso-paraffin, Olefin, Naphthene, and Aromatic) Characterization. It consists in using a constant slate of selected compounds that covers carbon number ranges of interest for the modeling of important and diverse refinery reactors such as hydrocrackers, reformers, and visbreakers based on the use of pre-set molecular structure groups.

Through combinations of these different component slates, designed to model paraffinic, olefinic, aromatic and other important chemical types typically encountered in oils, a feed’s measured distillation curve can be matched in a way similar to that of pseudo-components developed through standard oil characterization. The key advantage of this new method is the capture of the essential chemistry of the feedstock. The new method is flexible enough to encode in the characterized compounds known chemical characteristics of the feed ranging from simple properties such as molecular weight and density to PIONA characterization data. This new approach has been applied in VMGSim through two different utilities: PIONA Slate and Oil Source.

The PIONA Slate generates a group of pseudo component that represents a feed with different PIONA compounds that boils under a certain temperature range. The Oil Source is a utility that calculates the best combination of the PIONA Slate composition to match distillation and physical properties data.

Saturation pressure, molecular weight and density are used to divide each carbon number fraction into molecular groups. Only an extended oil analysis is needed to populate the PIONA slate. Allowing streams to be mixed, separated, fractionated and even reacted. Low permeability in certain shale plays can result in varying compositions at different wellheads geographically. When gathered, they can be combined into a single PIONA slate to capture the different compositions. A large network of wells and processing systems will use VMG-F.A.C.T.S. to manage the data effectively.

VMG invites you to send us your extended gas analysis. We will contact you to present the results of our PIONA based characterization tool, free of charge. Send your extended gas analysis to and don't forget to include your contact information. See how VMGSim can work for you and be the best at predicting your field data!

For more details on this characterization technique please refer to the following publication:

Hay, G.; Loria, H.; and Satyro, M.A. Thermodynamic Modeling and Process Simulation through PIONA Characterization, Energy and Fuels, 2013, 27 (6), 3578-3584.


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