| 000 | 01854cam a2200325 a 4500 | ||
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| 003 | EG-GiCUC | ||
| 005 | 20250223032433.0 | ||
| 008 | 191109s2019 ua db f m 000 0 eng d | ||
| 040 |
_aEG-GiCUC _beng _cEG-GiCUC |
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| 041 | 0 | _aeng | |
| 049 | _aDeposite | ||
| 097 | _aM.Sc | ||
| 099 | _aCai01.13.12.M.Sc.2019.Sa.Q | ||
| 100 | 0 | _aSamar Saied Abdelrady Shahat Hawary | |
| 245 | 1 | 0 |
_aQuantification and prediction of carbonate diagenesis from well logs and core data by artificial neural network / _cSamar Saied Abdelrady Shahat Hawary ; Supervised Abdulaziz M. Abdulaziz |
| 246 | 1 | 5 | _aالتقييم الكمى لتحوارت الصخور الجيرية والتنبؤ بها من معلومات تسجيلات الآبار واللباب الصخرى عن طريق الشبكات الاصطناعية العصبية |
| 260 |
_aCairo : _bSamar Saied Abdelrady Shahat Hawary , _c2019 |
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| 300 |
_a111 P. : _bcharts , maps ; _c30cm |
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| 502 | _aThesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Metallurgical Engineering | ||
| 520 | _aThe 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 | ||
| 530 | _aIssued also as CD | ||
| 653 | 4 | _aCarbonate reservoir | |
| 653 | 4 | _aDiagenesis Coefficient prediction | |
| 653 | 4 | _aWell logging | |
| 700 | 0 |
_aAbdulaziz M. Abdulaziz , _eSupervisor |
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| 856 | _uhttp://172.23.153.220/th.pdf | ||
| 905 |
_aNazla _eRevisor |
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| 905 |
_aShimaa _eCataloger |
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| 942 |
_2ddc _cTH |
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| 999 |
_c75062 _d75062 |
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