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Automatic well failure analysis for the sucker rod pumping systems / Ramez Maher Aziz Zaky Abdalla ; Supervised Ahmed Hamdy Elbanbi , Mahmoud Abuelela Mohamed

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Ramez Maher Aziz Zaky Abdalla , 2018Description: 85 P. : charts ; 30cmOther title:
  • تحليل مشاكل الآبار لمضخات العمود الماصة [Added title page title]
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  • Issued also as CD
Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Metallurgical Engineering Summary: 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%
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Item type Current library Home library Call number Copy number Status Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.12.M.Sc.2018.Ra.A (Browse shelf(Opens below)) Not for loan 01010110076428000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.12.M.Sc.2018.Ra.A (Browse shelf(Opens below)) 76428.CD Not for loan 01020110076428000

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%

Issued also as CD

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