Rate of penetration predictive model using artificial neural networks /
Mustafa Mohamed Amer
Rate of penetration predictive model using artificial neural networks / نموذج التنبؤ بمعدل الحفر باستخدام الشبكات العصبية الاصطناعية Mustafa Mohamed Amer ; Supervised Abdelsattar Dahab , Abdelalim Hashem Elsayed - Cairo : Mustafa Mohamed Amer , 2009 - 78 P. : charts ; 30cm
Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Metallurgical Engineering
Bit 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
ANN Bit ROP
Rate of penetration predictive model using artificial neural networks / نموذج التنبؤ بمعدل الحفر باستخدام الشبكات العصبية الاصطناعية Mustafa Mohamed Amer ; Supervised Abdelsattar Dahab , Abdelalim Hashem Elsayed - Cairo : Mustafa Mohamed Amer , 2009 - 78 P. : charts ; 30cm
Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Metallurgical Engineering
Bit 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
ANN Bit ROP