468687 A Game-Changer on the Modeling Strategy to Predict Asphaltene Precipitation at Reservoir Conditions

Monday, November 14, 2016: 10:00 AM
Union Square 22 (Hilton San Francisco Union Square)
Mohammad Tavakkoli, Andrew Chen and Francisco Vargas, Chemical & Biomolecular Engineering Department, Rice University, Houston, TX

The Perturbed Chain version of the Statistical Associating Fluid Theory Equation of State (PC-SAFT EoS) has been successfully applied to model the phase behavior of petroleum systems at reservoir conditions and the prediction of asphaltene precipitation. Although the predictive capabilities of this method and its wide range of applications are much better than other modeling techniques based on conventional cubic equations of state, there are certain features that are still missing, such as the polydisperse nature of asphaltenes. Furthermore, the simulation parameters are usually tuned to Asphaltene Onset Pressure (AOP) data –which are typically obtained by depressurizing live oil samples− or titration experiments of dead oil samples at ambient conditions. These techniques are subject to significant uncertainties caused by the slow kinetics of asphaltene aggregation and the limited sensitivity of the instruments used. The focus of this work is on the experimental study and modeling of asphaltene precipitation from n-alkane - titrated crude oil mixtures considering the polydispersity of asphaltenes. To investigate the effect of asphaltene polydispersity, a crude oil sample is studied by sequential precipitation with different normal alkanes. A recently developed experimental technique called the “Indirect Method” is used for the detection and quantification of asphaltene precipitation. Based on our previous research using the indirect method, it was found that both the onset and the amount of asphaltene precipitation can change with the aging time for a wide range of volume fractions of the added n-alkane. However, the data showed that the amount of precipitated asphaltene at 90 volume percent of a given precipitant is nearly time invariant and therefore this is used to tune the simulation parameters. A distribution function is proposed to represent the asphaltene molecular weight distribution for different asphaltene sub-fractions. It has been found that the inclusion of four asphaltene sub-fractions to represent the polydispersity of asphaltenes is necessary to match the amount of asphaltene precipitated. The modeling results show a reasonable agreement for the precipitated amount at 90 volume percent of n-pentane, n-hexane, n-heptane and n-octane. The PC-SAFT EOS predicts an onset point that requires less addition of n-alkane than the corresponding experimental point after one day of aging time. This result suggests that a thermodynamic limit exists for the onset of asphaltene precipitation and that it corresponds to experiments that require very long aging times, as it has been previously reported by Maqbool et al1. By fitting the simulation parameters to the amount of precipitated asphaltenes (and not the detected onset points) at 90 volume percent of added asphaltene precipitant, we can successfully remove the variability of the data with respect to the aging time. This tuning procedure also enables a more accurate prediction of the amount of precipitated asphaltenes at reservoir conditions, which is a more important result for the modeling of asphaltene deposition than the Asphaltene Onset Point itself. The integration of this novel characterization procedure as part of the Asphaltene Deposition Tool (ADEPT)2–4 developed at Rice University is a topic of ongoing research.



1. Maqbool, T.; Balgoa, A. T.; Fogler, H. S. Energy Fuels 2009, 23, 3681–3686. | 2. Vargas, F. M.; Creek, J. L.; Chapman, W. G. Energy Fuels 2010, 24, 2294–2299. | 3. Kurup, A. S.; Vargas, F. M.; Wang, J.; Buckley, J.; Creek, J. L.; Subramani, H., J.; Chapman, W. G. Energy Fuels 2011, 25, 4506–4516. | 4. Kurup, A. S.; Wang, J.; Subramani, H. J.; Buckley, J.; Creek, J. L.; Chapman, W. G. Energy Fuels 2012, 26, 5702–5710.

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