Zeeshan Tariq

About Zeeshan Tariq

Zeeshan Tariq, With an exceptional h-index of 28 and a recent h-index of 27 (since 2020), a distinguished researcher at King Fahd University of Petroleum and Minerals, specializes in the field of Machine Learning, Deep Learning, Reservoir Simulation, Petrophysics & Well Logging, Geomechanics.

His recent articles reflect a diverse array of research interests and contributions to the field:

Machine Learning accelerated Phase Flash Calculation for CO2–Brine System considering Capillarity Effect

Prediction of Foam Rheology Models Parameters Utilizing Machine Learning Tools

A Data-Infused Methodology for Estimating Swelling Potential in Shales Exposed to Various Completion Fluids

An encoder-decoder ConvLSTM surrogate model for simulating geological CO2 sequestration with dynamic well controls

Predicting Interfacial Tension in CO2/Brine Systems: A Data-Driven Approach and Its Implications for Carbon Geostorage

Physics-informed machine learning for reservoir management of enhanced geothermal systems

Prediction of NMR T2 Macro-and Micro-porosity with Machine Learning Techniques: Considering the constraints of ECS lithology classification

An Experimental Study and Machine Learning Modeling of Shale Swelling in Extended Reach Wells When Exposed to Diverse Water-Based Drilling Fluids

Zeeshan Tariq Information

University

Position

___

Citations(all)

2563

Citations(since 2020)

2367

Cited By

739

hIndex(all)

28

hIndex(since 2020)

27

i10Index(all)

74

i10Index(since 2020)

74

Email

University Profile Page

King Fahd University of Petroleum and Minerals

Google Scholar

View Google Scholar Profile

Zeeshan Tariq Skills & Research Interests

Machine Learning

Deep Learning

Reservoir Simulation

Petrophysics & Well Logging

Geomechanics

Top articles of Zeeshan Tariq

Title

Journal

Author(s)

Publication Date

Machine Learning accelerated Phase Flash Calculation for CO2–Brine System considering Capillarity Effect

Billal Aslam

Zeeshan Tariq

Bicheng Yan

2024/2/12

Prediction of Foam Rheology Models Parameters Utilizing Machine Learning Tools

ACS Omega

Jawad Al-Darweesh

Murtada Saleh Aljawad

Zeeshan Tariq

Shabeeb Alajmei

Bicheng Yan

...

2024/4/25

A Data-Infused Methodology for Estimating Swelling Potential in Shales Exposed to Various Completion Fluids

Mohammad Rasheed Khan

Zeeshan Tariq

Mobeen Murtaza

Bicheng Yan

Muhammad Shahzad Kamal

...

2024/2/12

An encoder-decoder ConvLSTM surrogate model for simulating geological CO2 sequestration with dynamic well controls

Gas Science and Engineering

Zhao Feng

Zeeshan Tariq

Xianda Shen

Bicheng Yan

Xuhai Tang

...

2024/4/6

Predicting Interfacial Tension in CO2/Brine Systems: A Data-Driven Approach and Its Implications for Carbon Geostorage

Mohammad Rasheed Khan

Zeeshan Tariq

Muhammad Ali

Mobeen Murtaza

2024/2/12

Physics-informed machine learning for reservoir management of enhanced geothermal systems

Geoenergy Science and Engineering

Bicheng Yan

Zhen Xu

Manojkumar Gudala

Zeeshan Tariq

Shuyu Sun

...

2024/3/1

Prediction of NMR T2 Macro-and Micro-porosity with Machine Learning Techniques: Considering the constraints of ECS lithology classification

Zhilei Han

Zeeshan Tariq

Bicheng Yan

Xinlei Shi

2024/2/12

An Experimental Study and Machine Learning Modeling of Shale Swelling in Extended Reach Wells When Exposed to Diverse Water-Based Drilling Fluids

Energy & Fuels

Zeeshan Tariq

Mobeen Murtaza

Salman Abdulrahman Alrasheed

Muhammad Shahzad Kamal

Bicheng Yan

...

2024/2/26

Fractured Geothermal Reservoir Using CO2 as Geofluid: Numerical Analysis and Machine Learning Modeling

ACS omega

Manojkumar Gudala

Zeeshan Tariq

Suresh Kumar Govindarajan

Bicheng Yan

Shuyu Sun

2024/2/6

Big Data Analysis Using Unsupervised Machine Learning: K-means Clustering and Isolation Forest Models for Efficient Anomaly Detection and Removal in Complex Lithologies

Aneeq Nasir Janjua

Abdulazeez Abdulraheem

Zeeshan Tariq

2024/2/12

Application of image processing techniques in deep-learning workflow to predict CO2 storage in highly heterogeneous naturally fractured reservoirs: A discrete fracture network …

Zeeshan Tariq

Bicheng Yan

Shuyu Sun

2023/3/7

A Robust General Physics-Informed Machine Learning Framework for Energy Recovery Optimization in Geothermal Reservoirs

Zhen Xu

Manojkumar Gudala

Bicheng Yan

Zeeshan Tariq

2023/6/5

Machine Learning Modeling of Saudi Arabian basalt/CO2/brine Wettability Prediction: Implications for CO2 Geo-Storage

Zeeshan Tariq

Muhammad Ali

Bicheng Yan

Shuyu Sun

Hussein Hoteit

2023/6/25

A Deep Learning Framework to Forecast Spatial-Temporal Dynamics of CO2 Mineralization in Reactive Rocks

Zeeshan Tariq

Bicheng Yan

Shuyu Sun

2023/10/2

Enhancing wettability prediction in the presence of organics for hydrogen geo-storage through data-driven machine learning modeling of rock/H2/brine systems

Fuel

Zeeshan Tariq

Muhammad Ali

Nurudeen Yekeen

Auby Baban

Bicheng Yan

...

2023/12/15

Physics Informed Surrogate Model Development in Predicting Dynamic Temporal and Spatial Variations During CO2 Injection into Deep Saline Aquifers

Zeeshan Tariq

Bicheng Yan

Shuyu Sun

2023/1/24

Data-driven machine learning modeling of mineral/CO2/brine wettability prediction: implications for CO2 geo-storage

Zeeshan Tariq

Muhammad Ali

Bicheng Yan

Shuyu Sun

Mohammad Khan

...

2023/3/7

Spatial–temporal prediction of minerals dissolution and precipitation using deep learning techniques: An implication to Geological Carbon Sequestration

Fuel

Zeeshan Tariq

Ertugrul Umut Yildirim

Manojkumar Gudala

Bicheng Yan

Shuyu Sun

...

2023/6/1

A Data-Driven Intelligent Approach to Predict Shear Wave Velocity in Shale Formations

Ayyaz Mustafa

Zeeshan Tariq

Arfa Iqbal

Maryum Naeem

2023/6/25

Doublet Huff and Puff (Dhp): A New Technology Towards Optimum Scco2 Sequestration with Stable Geothermal Recovery

Available at SSRN 4568399

Manojkumar Gudala

Bicheng Yan

Zeeshan Tariq

Shuyu Sun

2023/9/11

See List of Professors in Zeeshan Tariq University(King Fahd University of Petroleum and Minerals)

Co-Authors

H-index: 50
Shuyu Sun

Shuyu Sun

King Abdullah University of Science and Technology

H-index: 49
Mohamed A. Mahmoud

Mohamed A. Mahmoud

King Fahd University of Petroleum and Minerals

H-index: 43
Abdulazeez Abdulraheem

Abdulazeez Abdulraheem

King Fahd University of Petroleum and Minerals

H-index: 40
Muhammad Shahzad Kamal

Muhammad Shahzad Kamal

King Fahd University of Petroleum and Minerals

H-index: 36
Abdullah Sultan

Abdullah Sultan

King Fahd University of Petroleum and Minerals

H-index: 24
Dhafer Alshehri

Dhafer Alshehri

King Fahd University of Petroleum and Minerals

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