Future University In Egypt (FUE)
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Altagamoa Al Khames, Main centre of town, end of 90th Street
New Cairo
Faculty of Engineering & Technology
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Ismail Shaaban Ismail Mahgoub

Basic information

Name : Ismail Shaaban Ismail Mahgoub
Title: Professor
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Personal Info: Ismail Shaaban Ismail Mahgoub was born on 29/06/1951, Ph.d. from university of Nancy, France. Member of the society of Petroleum Engineers of the AIME. And Member of the Supreme Council of universities in Egypt. View More...


Certificate Major University Year
PhD Petroleum Engineering Nancy - France 1982
Diploma Petroleum Engineering Nancy - France 1979
Bachelor Petroleum Engineering Cairo - Egypt 1974

Researches /Publications

Removing the Outlier from the Production Data for the Decline Curve Analysis of Shale Gas Reservoirs: A Comparative Study Using Machine Learning

Ismail Shaaban Ismail Mahgoub

Hamid Khatab; Mahmoud Tantawy; Ismail Mahgoub



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Improving the Shale Gas Production Data Using the Angular- Based Outlier Detector Machine Learning Algorithm

Ismail Shaaban Ismail Mahgoub

Hamid Khatab; Mahmoud Tantawy



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Soft Computation Application: Utilizing Artificial Neural Network to Predict the Fluid Rate and Bottom Hole Flowing Pressure for Gas-lifted Oil Wells

Ismail Shaaban Ismail Mahgoub

Mazen Bahaa;Eissa Shokir



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A New Approach for Adjustment of PVT Data for Field Separator conditions

Ismail Shaaban Ismail Mahgoub

Eissa Mohamed Shokir(;) Mazen M.B.Hamed



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Gas Lift Optimization Using Artificial Neural Network and Integrated Production Modelling

Ismail Shaaban Ismail Mahgoub

Eissa Mohamed El-M. Shokir; OrcidMazen M. B. Hamed; Azza El-S. B. Ibrahim


the well flowing bottom hole pressure and fluid rates must be known for different applications in oil and gas industry. the current values of these parameters are necessary for different calculations such as gas lift optimization, well monitoring, reservoir performance, and field development plans. therefore, an artificial neural network(ANN) model was developed from an extracted data from PROSPER,PLT, and test seperator data. First the ANN model was trained and tested by synthetic data. Then, the ANN model was tested by a group of test points collected from the PLT reports. the developed ANN model yielded an accurate prediction of the well flowing bottom hole pressure and well fluid rate. the values of these parameters of each well are used to build an integrated production model IPM USING GAP software to perform different gas lift optimization scenarios

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Award Donor Date
Program Committee in MOC Conference Alex, Mediterranean Offshore Conference 2018
The Recognition of Excellence In Business Management city of Dubai, UAE on the 28th of September 2014
Assistantship from Institute National Polytechnique de lorrain Nancy, France 1978

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