A Comprehensive thermodynamic model has been developed in this laboratory which is capable of predicting fluid properties and simulating all the production scenarios:
Ten well-known equations of state (EOS) have been included in our in-house phase behaviour model where they can be employed to perform conventional as well as unconventional PVT tests at various conditions. The model is also capable of using a single EOS to describe the phase behaviour of reservoir fluids, particularly gas condensate and volatile oil systems, throughout the whole production process without any binary interaction parameter. Various modifications have been introduced to improve the predictive capability of the phase behaviour model. The inherent deficiencies of EOS, particularly for volatile oil and gas condensate samples, necessitate the calibration of phase behaviour model against experimental data. Various tuning methodologies have been developed in this laboratory. The developed methods are powerful tools which can be applied to any EOS to match the phase and volumetric behaviour of complex fluids (near critical) at reservoir and surface conditions. These methodologies along with the standard tuning procedures are implemented in the in-house phase behaviour model. |
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Accurate and reliable phase behaviour and volumetric data are essential elements for reservoir management. Reservoirs are generally produced by depletion where the reservoir pressure declines as fluids are recovered. Therefore, reservoir pressure is the main variable that determines the behaviour of fluids under reservoir conditions. Various PVT tests have been designed to study the fluid volumetric behaviour at reservoir and surface temperatures by varying the fluid pressure.
A compositional phase behaviour model, in principle, is capable of predicting all the PVT data, using only the composition of the original reservoir fluid. The models, however, are required to be evaluated and tuned against the measured PVT data prior to being used in reservoir studies with confidence.
Equation of state models can reliably predict the phase and volumetric behaviour of oil systems. However, their predictive capability deteriorates as the mixture approaches its critical point. Parameters of phase behaviour models are adjusted to match a set of measured data. The developed model is then used to predict the required data over a wide range of composition, pressure and temperature relevant to the recovery process.
The most common test on gas condensate and volatile oil samples is the constant composition expansion (CCE) test. In this test the phase and volumetric behaviour of the formation fluid is studied below the saturation pressure where a second phase appears. The acquired data along with measured volumetric behaviour from other depletion tests such as constant volume depletion (for gas condensate) and / or differential liberation (for oil samples) provide essential information for further reservoir exploitation.
| The capability of most widely used equations of state (EOS) in the industry, such as the Peng-Robinson and Soave-Redlich-Kwong EOS, to predict density is improved by including a third parameter, known as the volume translation factor. The third parameter in these EOS is usually calculated using a constant shift parameter for each component. A new method has been developed in this laboratory to determine the shift parameters of light hydrocarbons which has improved phase density predictions in gas condensate systems. |  |
| A knowledge of the viscosity of hydrocarbon fluids is needed to study the flow behaviour of mixtures in flow lines, well bore, and reservoir rock. Various viscosity models such as Lohrenz-Bray- Clark (LBC), one-reference fluid (CS1) and two-reference fluid (CS2) corresponding states principle are included in our phase behaviour model. The LBC correlation, which is widely used in petroleum industry, belongs to a group of models known as the residual viscosity method. The model has been modified in this laboratory by including the effect of molecular structure and the temperature on viscosity. |
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| Accurate and reliable information on interfacial tension (IFT) is of major importance in both petroleum and chemical engineering. The importance of IFT is magnified when dealing with IOR processes where the relative magnitude of interfacial (capillary), gravitational and viscous forces considerably affect the recovery of hydrocarbons. The two most commonly used methods in predicting the interfacial tension, i.e. the parachor method and the scaling law are included in our in-house phase behaviour model. Both these techniques have previously been evaluated and modified by this laboratory. |
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| Sampling is the most important element of any reservoir fluid analysis, yet major challenges are still hampering its efficient and low cost operation. Bottom hole samples of reservoir fluids can be captured using different techniques during well test, drill stem test or using wireline formation tester (WFT). If an oil-based mud is used in the drilling process, contamination of the reservoir fluid sample with drilling mud filtrate usually deteriorates the information provided by WFT samples.
The impact of contamination on the behaviour and properties of collected samples has been extensively studied in this laboratory. Various theoretical methodologies have been developed in this laboratory to reliably retrieve the composition of uncontaminated reservoir fluid from measured composition of contaminated samples. |
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| Using the developed methods, the retrieved composition of uncontaminated fluid is not physically available. A phase behaviour model is therefore required to predict the required information.
A phase behaviour model developed for contaminated samples may not be applicable to the original fluid. This is particularly important for gas condensate and volatile oil samples where the predicted results are quite sensitive to EOS parameters. An EOS-based method has also been developed in this laboratory to reliably determine the phase and volumetric behaviour of the uncontaminated fluid. In this method the parameters of the EOS are adjusted for the retrieved composition. |
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