MARC details
| 000 -LEADER |
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| fixed length control field |
02335cam a2200301 a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
EG-GiCUC |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
150614s2014 ua d f m 000 0 eng d |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
EG-GiCUC |
| Language of cataloging |
eng |
| Transcribing agency |
EG-GiCUC |
| 041 0# - LANGUAGE CODE |
|---|
| Language code of text/sound track or separate title |
eng |
| 049 ## - LOCAL HOLDINGS (OCLC) |
|---|
| Holding library |
Deposite |
| 097 ## - Thesis Degree |
|---|
| Thesis Level |
M.Sc |
| 099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC) |
|---|
| Classification number |
Cai01.13.12.M.Sc.2014.Ah.A |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Ahmad Mohamad Almaraghi |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Automatic reservoir model identification using artificial neural network in pressure transient analysis / |
| Statement of responsibility, etc. |
Ahmad Mohamad Almaraghi ; Supervised Ahmed H. Elbanbi |
| 246 15 - VARYING FORM OF TITLE |
|---|
| Title proper/short title |
استخدام الشبكات العصبية لتحديد نوع نموذج الخزان في تحليل الضغوط أليا |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Cairo : |
| Name of publisher, distributor, etc. |
Ahmad Mohamad Almaraghi , |
| Date of publication, distribution, etc. |
2014 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
75 P. : |
| Other physical details |
charts ; |
| Dimensions |
30cm |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Metallurgical Engineering |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
Oil and gas reservoirs are characterized by qualitative and quantitative values using pressure transient analysis. The well test is conducted by creating a flow disturbance in the well and recording the related response of the bottom-hole pressure. Well test analysis consists of two main phases: (1) the recognition of the entire reservoir model, and (2) the model parameter estimation. The objective of this study is to apply the Artificial Neural Network (ANN) technology to identify the reservoir model. A multilayer neural network had been used with back propagation optimization algorithm for the recognition process. The required training and test datasets have been generated by using the analytical solutions of commonly used reservoir models. Nine networks have been constructed; each one differentiates among six boundary models. Most commonly found reservoir models of different inner, outer boundary and reservoir medium are included (e.g. vertical, fracture and horizontal wells; homogenous, dual porosity and radial composite reservoirs; and infinite, one sealing fault, two sealing faults, rectangle and circle boundaries) |
| 530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE |
|---|
| Additional physical form available note |
Issued also as CD |
| 653 #4 - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
Artifical neural network |
| 653 #4 - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
Automatic reservoir model identification |
| 653 #4 - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
Pressure transient analysis |
| 700 0# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Ahmed Hamdy Elbanbi , |
| Relator term |
|
| 905 ## - LOCAL DATA ELEMENT E, LDE (RLIN) |
|---|
| Cataloger |
Nazla |
| Reviser |
Revisor |
| 905 ## - LOCAL DATA ELEMENT E, LDE (RLIN) |
|---|
| Cataloger |
Soheir |
| Reviser |
Cataloger |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Thesis |
