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Prediction of geomechanical properties from seismic attributes and well logs data analysis using artificial neural network in F3-block of the North Sea basin, offshore Netherlands / Hajir Oguz Hassan Almula ; Supervised Abdelsattar A. Dahab , Abdulaziz M. Abdulaziz

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Hajir Oguz Hassan Almula , 2018Description: 84 P. : charts , facsimiles ; 30cmOther title:
  • التنبؤ بالخصائص الجيوميكانيكية من تحليل السمات الزلزالية وبيانات تسجيلات الآبار بواسطة الشبكات العصبية الاصطناعية في القطعة "ف3" بحوض بحر الشمال-هولندا [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: This research aims to integrating seismic attributes and well data using supervised Artificial Neural Networks to identify Geomechanical Properties throughout F3-Block of the North Sea basin, Netherlands.This typically helps wellbore stability, drilling, and hydraulics fracturing. During the development, the engineers struggle to optimize drilling periods, reduce uncertainties and production costs, and make the best optimization of the use of available data. The verification analysis showed that property prediction achieved good results in Young{u2019}s modulus and Vp/Vs ratio but was marginal in Poisson{u2019}s ratio. Accordingly, results indicated a good potential of the proposed methodology in identifying geomechanical properties with accurate mapping and distribution throughout the pay zones and overlying sedimentary succession
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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.Ha.P (Browse shelf(Opens below)) Not for loan 01010110076840000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.12.M.Sc.2018.Ha.P (Browse shelf(Opens below)) 76840.CD Not for loan 01020110076840000

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

This research aims to integrating seismic attributes and well data using supervised Artificial Neural Networks to identify Geomechanical Properties throughout F3-Block of the North Sea basin, Netherlands.This typically helps wellbore stability, drilling, and hydraulics fracturing. During the development, the engineers struggle to optimize drilling periods, reduce uncertainties and production costs, and make the best optimization of the use of available data. The verification analysis showed that property prediction achieved good results in Young{u2019}s modulus and Vp/Vs ratio but was marginal in Poisson{u2019}s ratio. Accordingly, results indicated a good potential of the proposed methodology in identifying geomechanical properties with accurate mapping and distribution throughout the pay zones and overlying sedimentary succession

Issued also as CD

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