(122a) Tuning Equation-of-State to Match Experimental Data for near Critical Fluid: A Case Study of NKJ

Equations-of-state (EOS) are widely used in compositional simulation and surface facility design. Volatile oils and gas condensates require special treatment due to their near-critical nature; the classical material balance cannot be applied to these near-critical fluids because their molar compositions change as the reservoir is depleted. Thus, compositional simulation is necessary to determine the best economical and technical exploitation.   However, the performance of cubic equations of state is questionable in their predictive mode when they applied to petroleum mixtures. Tuning of the EOS is, generally, necessary for improving the predictions of compositional reservoir simulators, since the plus fraction of reservoir fluids has some uncertainty in its molecular weight and critical properties, equation-of-state, EOS, are generally not predictive without tuning its parameters to match experimental data.

We describe the optimum characterization and regression techniques that could provide tuned Equation-of-state for the near-critical volatile oil and gas condensate from the North Kuwait Jurassic reservoirs (KNJ). The PVT simulator PVTsim is used for the purpose. The results show that the implemented methods provide a reliable and efficient tool for representing PVT properties and phase behavior of the complex reservoir fluids. In this paper detailed of two samples is provided: one gas condensate and a volatile oil sample. We discuss the optimum sampling technique in this region and the quality control needed to perform the PVT analyses in order to ensure the validity of data to be used in EOS tuning.

Equations of state EOS parameters such as critical temperatures, critical pressures and acentric factors of the pseudo-components are matching parameters used to EOS tuning. In this study A characterization procedure is described to simulate the plus fractions of reservoir fluids and optimal selection of the regression parameters in determining the quality of the tuned fluid model.