Uncertainty in MMP Prediction from EOS Fluid Characterization
Published results on modelling crude oils by equation of states (EOS) showed the importance of tuning the equation to fit experimental data closely. Usually various parameters relating to C7+ such critical properties are altered to obtain an equation with a high regression correlation. Although curr...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Published: |
2011
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| Subjects: | |
| Online Access: | http://eprints.utp.edu.my/6074/1/SPE_144405.pdf http://eprints.utp.edu.my/6074/ |
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| Summary: | Published results on modelling crude oils by equation of states (EOS) showed the importance of tuning the equation to fit experimental data closely. Usually various parameters relating to C7+ such critical properties are altered to obtain an equation with a high regression correlation. Although current oil samples may be analysed up to C60+, the presence of isomers for each component may still cause a high degree of uncertainty in the averaged properties. The use of different tuning factors can also cause variations in the equation of states. In practice, the fluid model after tuning does not always give good prediction for all properties. For example, a good fit for saturation pressure and viscosity may give considerable difference in the density prediction. Another set of tuning may be excellent for another property and poor fit for another. Since the prediction of the minimum miscibility pressure (MMP) depends on the EOS model, variations in the model will result in differences in the MMP value. Therefore the priority set on each property during tuning may result in widely differing MMP values.
This research aims to find a relationship on the effects of each property of a crude oil on the MMP predicted. Peng-Robinson (PR) and Soave-Redlich-Kwong (SRK), five properties and key tuning parameters are varied. The impact of using different combinations of the critical properties correlations on MMP calculation are tested on gas/oil systems.
The best fit that is chosen from a tuning process may not always give a good fit to all properties. Our results may be used to list the priority of each property during tuning and will allow an estimation of the uncertainties in each property.
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