توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱Application of GK Fuzzy Clustering Method for PVT Analysis of Gas Condensate Reservoirs
نویسنده(ها): ، ،
اطلاعات انتشار: هفتمین کنگره ملی مهندسی شیمی، سال
تعداد صفحات: ۱۲
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 dataset with every cluster using an optimized membership function. GK forms a generalization of theFCM algorithm by utilizing the Mahalanobis distance for non–spherical clusters. In this clusteringalgorithm, components are placed in the hyper–component along with simultaneous calculation ofcritical and thermo–physical properties. Four case studies were selected for the characterization inPVT analysis of gas condensate reservoir fluids. 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 natureof pseudo–component generation. The perfect agreement between detailed and clustered PVTanalysis, shows good predicting capability of this clustering algorithm in mixture characterizationand pseudo–component generation to simulate thermodynamic equilibrium and volumetric behavior in PVT experiment designed for gas condensate reservoir including prediction of condensed liquid dropout, densities, viscosities and saturation pressure<\div>

۲Compositional Simulation of Gas Condensate Reservoirs with Optimal Hyper–Components Using Gustafson Kessel Method
نویسنده(ها): ، ،
اطلاعات انتشار: هفتمین کنگره ملی مهندسی شیمی، سال
تعداد صفحات: ۱۲
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 workGustafson–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 algorithmby 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 andthermo–physical properties calculation. Four case studies were selected for the simulation ofdepletion 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 samplesof reliable published data are used. The automatic placement of components in each group isconsistent with previous schemes those have highly heuristic nature of pseudo–component generation. The perfect agreement between, bottom–hole pressure, field average pressure andpressure distribution in the reservoir, both in detailed and clustered analysis, shows high predictingcapability 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 thehyper–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<\div>
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