Accurate prediction of gas-condensate reservoir performance below the saturation pressure is an inherent problem. This is due to the compositional variation and phase change during the depletion process. In doing so, ensuring accurate reservoir performance modelling for various pressure – volume – temperature (PVT) properties such as two phase “gas/condensate” viscosities and compressibility factor (Z factor) in desired reservoir conditions are particularly important. However, the existing viscosities and Z factor models cannot capture fluid flow complexity of gas-condensate reservoirs below the saturation pressure for modelling purposes.
The major contribution of the thesis is development of new gas/condensate viscosity and two-phase Z factor models using comprehensive experimental data sets. The data sets are representing downhole and reservoir condition. In the development process, an investigation on the use of soft computing techniques such as Support Vector Machine (SVM), Artificial Neural Network (ANN) and fuzzy logic (Mamdani & TSK) has been carried out. It is found that developed TSK fuzzy logic approaches offer the most accurate viscosity and two-phase Z factor prediction. The developed models can predict viscosity and two-phase Z factor of gas-condensate reservoirs in high pressure high temperature (HPHT) conditions with variety of non-hydrocarbon contents and they are not limited within geographical location.
The impact of viscosity and two-phase Z factor models towards the production calculation was the ultimate interest of this research. This led to further contribution on proposing the new method for computation of gas-condensate reservoir production rate performance, which involves integrating pseudopressure integral with volumetric material balance. For the computation of production rate, dynamic three-phase effective permeability has also been adopted. Distinctively, the proposed method provides better level of accuracy to compositional commercial simulation software in term of production forecast and economic impact of gas-condensate wells. Furthermore, the proposed method offers simpler computational procedures, where less input parameters are required.
|Date of Award||19 Feb 2021|
|Supervisor||Johnson Ugwu (Supervisor), Perk Lin Chong (Supervisor) & Farhad Nabhani (Supervisor)|