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Zeeshan Tariq
Zeeshan Tariq
KAUST (King Abdullah University of Science and Technology)
Verified email at kaust.edu.sa
Title
Cited by
Cited by
Year
Real time prediction of drilling fluid rheological properties using Artificial Neural Networks visible mathematical model (white box)
Salaheldin Elkatatny, Zeeshan Tariq, Mohamed Mahmoud
Journal of Petroleum Science and Engineering 146, 1202–1210, 2016
1662016
New insights into the prediction of heterogeneous carbonate reservoir permeability from well logs using artificial intelligence network
S Elkatatny, M Mahmoud, Z Tariq, A Abdulraheem
Neural Computing and Applications 30, 2673-2683, 2018
1292018
A systematic review of data science and machine learning applications to the oil and gas industry
Z Tariq, MS Aljawad, A Hasan, M Murtaza, E Mohammed, A El-Husseiny, ...
Journal of Petroleum Exploration and Production Technology, 1-36, 2021
772021
An integrated approach for estimating static Young’s modulus using artificial intelligence tools
S Elkatatny, Z Tariq, M Mahmoud, A Abdulraheem, I Mohamed
Neural Computing and Applications 31, 4123-4135, 2019
772019
A new artificial intelligence based empirical correlation to predict sonic travel time
Z Tariq, S Elkatatny, M Mahmoud, A Abdulraheem
International Petroleum Technology Conference, D012S057R001, 2016
762016
Relative contribution of wettability Alteration and interfacial tension reduction in EOR: A critical review
X Deng, Z Tariq, M Murtaza, S Patil, M Mahmoud, MS Kamal
Journal of Molecular Liquids 325, 115175, 2021
732021
Optimization of rate of penetration using artificial intelligent techniques
SM Elkatatny, Z Tariq, MA Mahmoud, A Al-AbdulJabbar
ARMA US Rock Mechanics/Geomechanics Symposium, ARMA-2017-0429, 2017
732017
Development of new mathematical model for compressional and shear sonic times from wireline log data using artificial intelligence neural networks (white box)
S Elkatatny, Z Tariq, M Mahmoud, I Mohamed, A Abdulraheem
Arabian Journal for Science and Engineering 43 (11), 6375-6389, 2018
692018
A new technique to develop rock strength correlation using artificial intelligence tools
Z Tariq, S Elkatatny, M Mahmoud, AZ Ali, A Abdulraheem
SPE Reservoir Characterisation and Simulation Conference and Exhibition …, 2017
682017
New insights into porosity determination using artificial intelligence techniques for carbonate reservoirs
S Elkatatny, Z Tariq, M Mahmoud, A Abdulraheem
Petroleum 4 (4), 408-418, 2018
642018
Machine learning derived correlation to determine water saturation in complex lithologies
MR Khan, Z Tariq, A Abdulraheem
SPE Kingdom of Saudi Arabia annual technical symposium and exhibition, SPE …, 2018
632018
A new approach to predict failure parameters of carbonate rocks using artificial intelligence tools
Z Tariq, S Elkatatny, M Mahmoud, AZ Ali, A Abdulraheem
SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition …, 2017
602017
Machine learning application for oil rate prediction in artificial gas lift wells
MR Khan, S Alnuaim, Z Tariq, A Abdulraheem
SPE middle east oil and gas show and conference, D032S085R002, 2019
532019
A review on non-aqueous fracturing techniques in unconventional reservoirs
S Kalam, C Afagwu, J Al Jaberi, OM Siddig, Z Tariq, M Mahmoud, ...
Journal of Natural Gas Science and Engineering 95, 104223, 2021
462021
Application of artificial intelligent techniques to determine sonic time from well logs
SM Elkatatny, T Zeeshan, M Mahmoud, A Abdulazeez, IM Mohamed
ARMA US Rock Mechanics/Geomechanics Symposium, ARMA-2016-755, 2016
442016
A holistic approach to develop new rigorous empirical correlation for static Young's modulus
Z Tariq, S Elkatatny, M Mahmoud, A Abdulraheem
Abu Dhabi International Petroleum Exhibition and Conference, D031S067R003, 2016
402016
Estimation of Rock Mechanical Parameters Using Artificial Intelligence Tools
Z Tariq, SM Elkatatny, MA Mahmoud, A Abdulraheem
51st U.S. Rock Mechanics/Geomechanics Symposium, 2017
382017
Gas adsorption and reserve estimation for conventional and unconventional gas resources
AE Radwan, DA Wood, M Mahmoud, Z Tariq
Sustainable geoscience for natural gas subsurface systems, 345-382, 2022
362022
Data-driven machine learning approach to predict mineralogy of organic-rich shales: An example from Qusaiba Shale, Rub’al Khali Basin, Saudi Arabia
A Mustafa, Z Tariq, M Mahmoud, AE Radwan, A Abdulraheem, ...
Marine and Petroleum Geology 137, 105495, 2022
352022
Application of artificial intelligence to estimate oil flow rate in gas-lift wells
MR Khan, Z Tariq, A Abdulraheem
Natural Resources Research 29 (6), 4017-4029, 2020
352020
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