Thesis (Ph.D.) - Cairo University - Faculty of Engineering - Department of Metallurgical Engineering

In absence of PVT laboratory experiments data on representative fluid samples, it is usually difficult to choose the appropriate PVT correlations to calculate oil PVT properties. This difficulty will be increase when input data to PVT correlations (oil API gravity, initial gas-oil ratio, specific gravity of separator gas and temperature) vary along the flow from one section to the other in the production system. In this research, two Hybrid models and one Expert System Artificial Intelligent (AI) approaches are introduced for solving this problem. The proposed approaches are based on clustering the PVT data into several groups. According to this study, expert guidelines are proposed to select an appropriate correlation for each oil properties. The guidelines for using hybrid AI and the best configuration are also added for calculating oil PVT properties

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