Compositional Simulation of Gas Condensate Reservoirs with Optimal Hyper-Components Using Gustafson Kessel Method

The use of hyper-components (pseudo-components) is a prevailing approach to make the compositional simulation practical regarding CPU time and memory economy. In this work Gustafson-Kessel (GK) FCM algorithm was implemented and its effectiveness in handling high dimensional data was revealed. This algorithm associates each data point in the data set with every cluster using an optimized membership function. GK forms a generalization of the FCM algorithm by utilizing the Mahalanobis distance for non-spherical clusters. In this clustering algorithm, components are placed in the hyper-component along with simultaneous calculation of critical and thermo-physical properties calculation. Four case studies were selected for the simulation of depletion and miscible injection processes in the gas condensate reservoirs to show effectiveness of the clustering algorithm. The mixture composition and properties of the gas condensate samples of reliable published data are used. The automatic placement of components in each group is consistent with previous schemes those have highly heuristic nature of pseudo-component generation. The perfect agreement between, bottom-hole pressure, field average pressure and pressure distribution in the reservoir, both in detailed and clustered analysis, shows high predicting capability of clustering algorithms for molar density and viscosity calculation in the compositional reservoir simulation. To include the effect of composition, at the end of defuzzification phase the hyper-component centers are updated based on mole fraction of the components in a hypercomponent. The condensed hydrocarbon production rate for compositional simulation with both detailed and clustered PVT analysis shows reliability of GK for fluid characterization for gas condensate reservoir compositional simulation