Quantification and prediction of carbonate diagenesis from well logs and core data by artificial neural network / Samar Saied Abdelrady Shahat Hawary ; Supervised Abdulaziz M. Abdulaziz

By:

Samar Saied Abdelrady Shahat Hawary

Contributor(s):

Abdulaziz M. Abdulaziz []

Material type: Text

TextLanguage: English Publication details: Cairo : Samar Saied Abdelrady Shahat Hawary , 2019Description: 111 P. : charts , maps ; 30cmOther title:

التقييم الكمى لتحوارت الصخور الجيرية والتنبؤ بها من معلومات تسجيلات الآبار واللباب الصخرى عن طريق الشبكات الاصطناعية العصبية [Added title page title]

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Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Metallurgical Engineering Summary: The application of artificial neural networks (ANNs) is used successfully to generate a numerical scale for diagenesis quantification from 0 to 10 with specified particular range for each type of diagenesis. It enhances identifying the rock typing and generated a link between geological and reservoir modeling. Also, a mathematical correlation is generated to directly predict the quantification of diagenesis in carbonate rocks in the study area

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Item type	Current library	Home library	Call number	Copy number	Status	Barcode
Thesis	قاعة الرسائل الجامعية - الدور الاول	المكتبة المركزبة الجديدة - جامعة القاهرة	Cai01.13.12.M.Sc.2019.Sa.Q (Browse shelf(Opens below))		Not for loan	01010110079839000
CD - Rom	مخـــزن الرســائل الجـــامعية - البدروم	المكتبة المركزبة الجديدة - جامعة القاهرة	Cai01.13.12.M.Sc.2019.Sa.Q (Browse shelf(Opens below))	79839.CD	Not for loan	01020110079839000

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

The application of artificial neural networks (ANNs) is used successfully to generate a numerical scale for diagenesis quantification from 0 to 10 with specified particular range for each type of diagenesis. It enhances identifying the rock typing and generated a link between geological and reservoir modeling. Also, a mathematical correlation is generated to directly predict the quantification of diagenesis in carbonate rocks in the study area

Issued also as CD

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