000			02181cam a2200337 a 4500
003			EG-GiCUC
005			20250223032150.0
008			190115s2018 ua o f m 000 0 eng d
040			_aEG-GiCUC _beng _cEG-GiCUC
041	0		_aeng
049			_aDeposite
097			_aM.Sc
099			_aCai01.13.12.M.Sc.2018.Mo.A
100	0		_aMohamed Atta Farahat Mohamed
245	1	0	_aArtificial intelligence applications for pore pressure and fracture pressure prediction from seismic attributes analysis and well logs data / _cMohamed Atta Farahat Mohamed ; Supervised Abdelalim Hashem Elsayed , Abdulaziz Mohamed Abdulaziz
246	1	5	_aتطبيقات الذكاء الاصطناعى فى التنبؤ بضغط المسام و ضغط الكسر بتحليل بيانات السمات الزلزالية السيزمية وتسجيلات الابار
260			_aCairo : _bMohamed Atta Farahat Mohamed , _c2018
300			_a103 P. : _bphotographs ; _c30cm
502			_aThesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Metallurgical Engineering
520			_aThis study aims to investigate the pore and fracture pressure of sub-surface formations. Eaton{u2019}s method is applied to predict pore and fracture pressure of wells. Inversion process with numerous algorithms are applied to seismic area of the field. Prediction methods are applied to investigate best attributes such as single, multiple seismic attribute analysis and neural network. Well logs and seismic attributes obtained from inversion process and seismic data are used to train ANN. ANN is validated using blind wells which are not included in training process. The correlations of ANN training and validation are good so ANN is applied for prediction of pore and fracture pressure for 3D seismic area of field
530			_aIssued also as CD
653		4	_aFracture pressure
653		4	_aNeural network
653		4	_aPore pressure
700	0		_aAbdelalim Hashem Elsayed , _eSupervisor
700	0		_aAbdulaziz Mohamed Abdulaziz , _eSupervisor
856			_uhttp://172.23.153.220/th.pdf
905			_aNazla _eRevisor
905			_aSamia _eCataloger
942			_2ddc _cTH
999			_c69597 _d69597