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A hybrid approach based on artificial neural network and integrated production modeling for gas lift optimization / Mazen Mohamed Bahaa Eldin Hussein Hamed ; Supervised Eissa Mohamed Shokir , Ismail Mahgoub

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Mazen Mohamed Bahaa Eldin Hussein Hamed , 2016Description: 74 P., (1) Folded page of platas : plans ; 30cmOther title:
  • طريقة مزدوجة لترشيد الغازعن طريق شبكات الخلايا العصبية الاصطناعية و النمذجة المتكاملة لعمليات الانتاج [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: An artificial neural network model was developed to predict the values of the bottom hole flowing pressure and the total fluid rate per each well using the available field parameters like the water cut samples, static pressure surveys, reservoir gas oil ratio, the well head temperature and pressure in addition to the gas injection rate and gas injection pressure. This developed ANN used in building accurate individual well models on PROSPER and a full field network model gathering all the individuals' models with the surface network. This creates an integrated production model aiming to perform field wide gas lift optimization
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Item type Current library Home library Call number Copy number Status Date due Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.12.M.Sc.2016.Ma.H (Browse shelf(Opens below)) Not for loan 01010110071974000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.12.M.Sc.2016.Ma.H (Browse shelf(Opens below)) 71974.CD Not for loan 01020110071974000

Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Metallurgical Engineering

An artificial neural network model was developed to predict the values of the bottom hole flowing pressure and the total fluid rate per each well using the available field parameters like the water cut samples, static pressure surveys, reservoir gas oil ratio, the well head temperature and pressure in addition to the gas injection rate and gas injection pressure. This developed ANN used in building accurate individual well models on PROSPER and a full field network model gathering all the individuals' models with the surface network. This creates an integrated production model aiming to perform field wide gas lift optimization

Issued also as CD

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