Author/Authors :
Mohammad hossein Heidari Sureshjani National Iranian Oil Company-IOR Research Institute, Tehran , Ehsan Sedarat National Iranian Oil Company-IOR Research Institute, Tehran , Mahnaz Hekmatzadeh National Iranian Oil Company-IOR Research Institute, Tehran , Mohammad Mirzadeh National Iranian Oil Company-IOR Research Institute, Tehran , Habib Valiolahi National Iranian Oil Company-IOR Research Institute, Tehran , Shahab Gerami National Iranian Oil Company-IOR Research Institute, Tehran , Emad Roayayi National Iranian Oil Company-IOR Research Institute, Tehran , Mohammadali Emadi National Iranian Oil Company-IOR Research Institute, Tehran
كليدواژه :
Gas/condensate , PVT , Positive coupling , Non-Darcy flow
چكيده لاتين :
Formation of condensate-bank in near well region of a gas/condensate reservoir is a well known phenomenon which leads to a severe loss of well productivity and therefore lowers gas recovery. An optimized plan for development of any gas/condensate reservoir requires in-dlepth knowledge of phase and flow behavior as well as their effects on reservoir performance. This paper presents the effects of important reservoir parameters on performance prediction of a 4layered gas/condensate reservoir. For this purpose, a finely-gridded single well model is constructed using a compositional reservoir simulator. The model has four producing layers with no cross-flow. In addition, rock and fluid properties of a giant gas/condensate reservoir in Middle East are considered to define a base case. Considering production life of the reservoir, the model is used to investigate the effects of uncertainties in the input parameters such as compositions, molecular weight of fluid heavy end fraction, and velocity dependent relative permeability parameters on both of well and reservoir performances. The results show that some parameters may significantly affect the production plateau time and therefore field development plan. The results also highlight and emphasis on doing appropriate SCAL to reduce the risk of data uncertainty on production performance prediction.