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Prediction of hydraulic properties in carbonate reservoirs using artificial neural network / Khalaf Gad Salem Ibrahim ; Supervised Abdelsattar A. Dahab , Abdulaziz M. Abdulaziz

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Khalaf Gad Salem Ibrahim , 2017Description: 105 P. : charts , facsimiles ; 30cmOther title:
  • التنبؤ بالخواص الهيدروليكية فى الخزانات الجيرية باستخدام الشبكة العصبية الصناعية [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: The success of applying artificial neural networks (ANNs) to solve complex engineering problems has drawn attention to its potential applications in the petroleum industry especially in formation evaluation. In the present study, models are developed to predict the hydraulic properties (porosity and permeability) in carbonate reservoir from well logging measurements using ANN. The developed ANN model for carbonate reservoir is constructed and validated using numerous dataset collected from various worldwide carbonate reservoirs
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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.2017.Kh.P (Browse shelf(Opens below)) Not for loan 01010110074230000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.12.M.Sc.2017.Kh.P (Browse shelf(Opens below)) 74230.CD Not for loan 01020110074230000

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

The success of applying artificial neural networks (ANNs) to solve complex engineering problems has drawn attention to its potential applications in the petroleum industry especially in formation evaluation. In the present study, models are developed to predict the hydraulic properties (porosity and permeability) in carbonate reservoir from well logging measurements using ANN. The developed ANN model for carbonate reservoir is constructed and validated using numerous dataset collected from various worldwide carbonate reservoirs

Issued also as CD

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