Automatic well failure analysis for the sucker rod pumping systems /
Ramez Maher Aziz Zaky Abdalla
Automatic well failure analysis for the sucker rod pumping systems / تحليل مشاكل الآبار لمضخات العمود الماصة Ramez Maher Aziz Zaky Abdalla ; Supervised Ahmed Hamdy Elbanbi , Mahmoud Abuelela Mohamed - Cairo : Ramez Maher Aziz Zaky Abdalla , 2018 - 85 P. : charts ; 30cm
Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Metallurgical Engineering
Sucker rod pumping is one of the most common forms of the artificial lift technologies. Monitoring the working condition of the sucker rod pumping system is a hard task which requires several details in order to sustain acceptable productivity levels. Hence, a description model for the dynamometer cards was established. Then, machine learning techniques were trained to predict downhole pumping condition. The proposed model is trained by using real field data of about 6,385 dynamometer cards. The results show that the developed model achieved Percent Error of 1.51%
Back Propagation Neural Networks (BPNN) Sucker Rod Pumping System Support Vector Machine (SVM)
Automatic well failure analysis for the sucker rod pumping systems / تحليل مشاكل الآبار لمضخات العمود الماصة Ramez Maher Aziz Zaky Abdalla ; Supervised Ahmed Hamdy Elbanbi , Mahmoud Abuelela Mohamed - Cairo : Ramez Maher Aziz Zaky Abdalla , 2018 - 85 P. : charts ; 30cm
Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Metallurgical Engineering
Sucker rod pumping is one of the most common forms of the artificial lift technologies. Monitoring the working condition of the sucker rod pumping system is a hard task which requires several details in order to sustain acceptable productivity levels. Hence, a description model for the dynamometer cards was established. Then, machine learning techniques were trained to predict downhole pumping condition. The proposed model is trained by using real field data of about 6,385 dynamometer cards. The results show that the developed model achieved Percent Error of 1.51%
Back Propagation Neural Networks (BPNN) Sucker Rod Pumping System Support Vector Machine (SVM)