Zeeshan Tariq

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82 Conference contribution
69 Article
2 Chapter

A Deep Learning Framework to Forecast Spatial-Temporal Dynamics of CO₂ Mineralization in Reactive Rocks
Tariq, Z., Yan, B. & Sun, S., 2023, Society of Petroleum Engineers - ADIPEC, ADIP 2023. Society of Petroleum Engineers, (Society of Petroleum Engineers - ADIPEC, ADIP 2023).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
A fast method to infer Nuclear Magnetic Resonance based effective porosity in carbonate rocks using machine learning techniques
Tariq, Z., Gudala, M., Yan, B., Sun, S. & Mahmoud, M., Jan 11 2023, In: Geoenergy Science and Engineering. p. 211333
Research output: Contribution to journal › Article › peer-review
10 Scopus citations
An Intelligent Safe Well Bottom-Hole Pressure Monitoring of CO2 Injection Well into Deep Saline: A coupled Hydro-Mechanical Approach
Tariq, Z., Yan, B. & Sun, S., Mar 7 2023, Day 1 Sun, February 19, 2023. SPE
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
3 Scopus citations
Application of a Novel Green and Biocompatible Clay Swelling Inhibitor in Fracturing Fluid Design
Murtaza, M., Tariq, Z., Kamal, M. S., Rana, A., Patil, S., Mahmoud, M. & Al-Shehri, D., May 15 2023, Day 3 Wed, May 24, 2023. SPE
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Application of Image Processing Techniques in Deep-Learning Workflow to Predict CO2 Storage in Highly Heterogeneous Naturally Fractured Reservoirs: A Discrete Fracture Network Approach
Tariq, Z., Yan, B. & Sun, S., Mar 7 2023, Day 1 Sun, February 19, 2023. SPE
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
2 Scopus citations
A Robust General Physics-Informed Machine Learning Framework for Energy Recovery Optimization in Geothermal Reservoirs
Xu, Z., Gudala, M., Yan, B., Tariq, Z. & Abdullah, K., 2023, Society of Petroleum Engineers - SPE EuropEC - Europe Energy Conference featured at the 84th EAGE Annual Conference and Exhibition, EURO 2023. Society of Petroleum Engineers, (Society of Petroleum Engineers - SPE EuropEC - Europe Energy Conference featured at the 84th EAGE Annual Conference and Exhibition, EURO 2023).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
4 Scopus citations
Data-Driven Machine Learning Modeling of Mineral/CO₂/Brine Wettability Prediction: Implications for CO₂ Geo-Storage
Tariq, Z., Ali, M., Yan, B., Sun, S., Khan, M., Yekeen, N. & Hoteit, H., 2023, Society of Petroleum Engineers - Middle East Oil, Gas and Geosciences Show, MEOS 2023. Society of Petroleum Engineers (SPE), (SPE Middle East Oil and Gas Show and Conference, MEOS, Proceedings).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
11 Scopus citations
Deep-Learning-Based Surrogate Model to Predict CO2 Saturation Front in Highly Heterogeneous Naturally Fractured Reservoirs: A Discrete Fracture Network Approach
Tariq, Z., Xu, Z., Gudala, M., Yan, B. & Sun, S., Jan 24 2023, Day 2 Wed, January 25, 2023. SPE
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
4 Scopus citations

Researchers' Work from King Abdullah University of Science and Technology (KAUST) Focuses on Chemicals and Chemistry (Towards Rapid Prediction of Nuclear Magnetic Resonance-based Bimodal Porosities: an Example From the Middle Eastern Carbonate ...)
Yan, B. & Tariq, Z.
07/30/24
1 item of Media coverage
Press/Media: Press / Media
Reports on Machine Learning Findings from King Fahd University of Petroleum and Minerals Provide New Insights (Prediction of Foam Rheology Models Parameters Utilizing Machine Learning Tools)
Yan, B. & Tariq, Z.
05/22/24
1 item of Media coverage
Press/Media: Press / Media
Researchers from King Abdullah University of Science and Technology (KAUST) Report Details of New Studies and Findings in the Area of Machine Learning (Physics-informed Machine Learning for Reservoir Management of Enhanced Geothermal Systems)
Sun, S., Finkbeiner, T., Yan, B., Gudala, M. & Tariq, Z.
04/9/24
1 item of Media coverage
Press/Media: Press / Media
Findings in Machine Learning Reported from King Abdullah University of Science and Technology (KAUST) (An Experimental Study and Machine Learning Modeling of Shale Swelling In Extended Reach Wells When Exposed To Diverse Water-based Drilling ...)
Yan, B. & Tariq, Z.
04/9/24
1 item of Media coverage
Press/Media: Press / Media
King Abdullah University of Science and Technology (KAUST) Reports Findings in Machine Learning (Fractured Geothermal Reservoir Using CO2 as Geofluid: Numerical Analysis and Machine Learning Modeling)
Sun, S., Yan, B., Gudala, M. & Tariq, Z.
03/13/24
1 item of Media coverage
Press/Media: Press / Media
Investigators at King Abdullah University of Science and Technology (KAUST) Report Findings in Machine Learning (Enhancing Wettability Prediction In the Presence of Organics for Hydrogen Geo-storage Through Data-driven Machine Learning Modeling ...)
Sun, S., Hoteit, H., Ali, M., Yan, B., Tariq, Z. & Ali, M.
12/14/23
1 item of Media coverage
Press/Media: Press / Media
King Abdullah University of Science and Technology (KAUST) Reports Findings in Machine Learning (Predicting wettability of mineral/CO2/brine systems via data-driven machine learning modeling: Implications for carbon geo-sequestration)
Sun, S., Hoteit, H., Ali, M., Yan, B., Tariq, Z. & Ali, M.
10/31/23
1 item of Media coverage
Press/Media: Press / Media
Reports Summarize Machine Learning Study Results from King Abdullah University of Science and Technology (KAUST) (A Fast Method To Infer Nuclear Magnetic Resonance Based Effective Porosity In Carbonate Rocks Using Machine Learning Techniques)
Sun, S., Yan, B., Gudala, M. & Tariq, Z.
08/22/23
1 item of Media coverage
Press/Media: Press / Media

Zeeshan Tariq

Fingerprint

Collaborations and top research areas from the last five years

Dive into details

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

A fast method to infer Nuclear Magnetic Resonance based effective porosity in carbonate rocks using machine learning techniques

An Intelligent Safe Well Bottom-Hole Pressure Monitoring of CO2 Injection Well into Deep Saline: A coupled Hydro-Mechanical Approach

Application of a Novel Green and Biocompatible Clay Swelling Inhibitor in Fracturing Fluid Design

Application of Image Processing Techniques in Deep-Learning Workflow to Predict CO2 Storage in Highly Heterogeneous Naturally Fractured Reservoirs: A Discrete Fracture Network Approach

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

Data-Driven Machine Learning Modeling of Mineral/CO₂/Brine Wettability Prediction: Implications for CO₂ Geo-Storage

Deep-Learning-Based Surrogate Model to Predict CO2 Saturation Front in Highly Heterogeneous Naturally Fractured Reservoirs: A Discrete Fracture Network Approach

Researchers' Work from King Abdullah University of Science and Technology (KAUST) Focuses on Chemicals and Chemistry (Towards Rapid Prediction of Nuclear Magnetic Resonance-based Bimodal Porosities: an Example From the Middle Eastern Carbonate ...)

Reports on Machine Learning Findings from King Fahd University of Petroleum and Minerals Provide New Insights (Prediction of Foam Rheology Models Parameters Utilizing Machine Learning Tools)

Researchers from King Abdullah University of Science and Technology (KAUST) Report Details of New Studies and Findings in the Area of Machine Learning (Physics-informed Machine Learning for Reservoir Management of Enhanced Geothermal Systems)

Findings in Machine Learning Reported from King Abdullah University of Science and Technology (KAUST) (An Experimental Study and Machine Learning Modeling of Shale Swelling In Extended Reach Wells When Exposed To Diverse Water-based Drilling ...)

King Abdullah University of Science and Technology (KAUST) Reports Findings in Machine Learning (Fractured Geothermal Reservoir Using CO2 as Geofluid: Numerical Analysis and Machine Learning Modeling)

Investigators at King Abdullah University of Science and Technology (KAUST) Report Findings in Machine Learning (Enhancing Wettability Prediction In the Presence of Organics for Hydrogen Geo-storage Through Data-driven Machine Learning Modeling ...)

King Abdullah University of Science and Technology (KAUST) Reports Findings in Machine Learning (Predicting wettability of mineral/CO2/brine systems via data-driven machine learning modeling: Implications for carbon geo-sequestration)

Reports Summarize Machine Learning Study Results from King Abdullah University of Science and Technology (KAUST) (A Fast Method To Infer Nuclear Magnetic Resonance Based Effective Porosity In Carbonate Rocks Using Machine Learning Techniques)

Zeeshan Tariq

Fingerprint

Collaborations and top research areas from the last five years

Research output

Press/Media