000			01603cam a2200337 a 4500
003			EG-GiCUC
005			20250223030331.0
008			110103s2009 ua d 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.2009.Mu.R
100	0		_aMustafa Mohamed Amer
245	1	0	_aRate of penetration predictive model using artificial neural networks / _cMustafa Mohamed Amer ; Supervised Abdelsattar Dahab , Abdelalim Hashem Elsayed
246	1	5	_aنموذج التنبؤ بمعدل الحفر باستخدام الشبكات العصبية الاصطناعية
260			_aCairo : _bMustafa Mohamed Amer , _c2009
300			_a78 P. : _bcharts ; _c30cm
502			_aThesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Metallurgical Engineering
520			_aBit performance is a key factor to improve drilling performance and reduce drilling costs . The objective of the research is to provide a tool that can predict ROP in order to optimize bit selection . The specific goal of this study is a mean to predict ROP using Artificial Neural Networks ( ANN ) model . Lithology changes , drilling parameters data and bit data were the inputs of our model
530			_aIssued also as CD
653		4	_aANN
653		4	_aBit
653		4	_aROP
700	0		_aAbdelalim Hashem Elsayed , _eSupervisor
700	0		_aAbdelsattar Dahab , _eSupervisor
856			_uhttp://172.23.153.220/th.pdf
905			_aFatma _eCataloger
905			_aNazla _eRevisor
942			_2ddc _cTH
999			_c32492 _d32492