000			02276cam a2200349 a 4500
003			EG-GiCUC
005			20250223031450.0
008			160405s2015 ua d f m 000 0 eng d
040			_aEG-GiCUC _beng _cEG-GiCUC
041	0		_aeng
049			_aDeposite
097			_aPh.D
099			_aCai01.13.12.Ph.D.2015.Ab.D
100	0		_aAbdelrigeeb Ali Algathe
245	1	0	_aDevelopment artificial intelligent systems for black oil pvt properties prediction / _cAbdelrigeeb Ali Algathe ; Supervised Khaled A. Abdelfattah , Khaled A, Elmetwally , Ahmed H. Elbanbi
246	1	5	_aتطويــرانظمة الذكاء الصناعي للتنبؤ بخصائص النفط تحت ظروف الضغط والحجم والحرارة
260			_aCairo : _bAbdelrigeeb Ali Algathe , _c2015
300			_a218 P. : _bcharts ; _c30cm
502			_aThesis (Ph.D.) - Cairo University - Faculty of Engineering - Department of Metallurgical Engineering
520			_aIn 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
530			_aIssued also as CD
653		4	_aExpert System
653		4	_aHybrid
653		4	_aOil Properties
700	0		_aAhmed Hamdy Elbanbi , _eSupervisor
700	0		_aKhaled A, Elmetwally , _eSupervisor
700	0		_aKhaled Ahmed Abdelfattah , _eSupervisor
856			_uhttp://172.23.153.220/th.pdf
905			_aNazla _eRevisor
905			_aSoheir _eCataloger
942			_2ddc _cTH
999			_c55853 _d55853