Zeeshan Tariq

Postdoctoral Fellows

Post-doctoral Fellow

Research Interest

My research interests lie in the areas of unconventional reservoirs characterization, machine learning, reservoir simulation, well logging, petrophysics, geomechanics, hydraulic fracturing, matrix stimulation, acidization, formation evaluation, production engineering, drilling fluids, filter cake removal, and formation damage mitigation.

Selected Publications

  • Machine learning approach to predict the dynamic linear swelling of shales treated with different waterbased drilling fluids
    Z Tariq, M Murtaza, M Mahmoud, MS Aljawad, MS Kamal, Fuel, 315, 123282, (2022)
  • 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 11 (12), 4339-4374, (2022)
  • Reduction of Breakdown Pressure by Filter Cake Removal Using Thermochemical Fluids and Solvents: Experimental and Numerical Studies
    Z Tariq, MS Aljawad, M Mahmoud, O Alade, MS Kamal, A Al-Nakhli
    Molecules 26 (15), 4407, (2021)
  • Comparative Study of Fracture Conductivity in Various Carbonate Rocks Treated with GLDA Chelating Agent and HCl Acid
    Z Tariq, A Hassan, R Al-Abdrabalnabi, MS Aljawad, M Mahmoud
    Energy & Fuels 35 (23), 19641-19654, (2021)
  • Thermochemical acid fracturing of tight and unconventional rocks: Experimental and modeling investigations
    Z Tariq, MS Aljawad, M Mahmoud, A Abdulraheem, AR Al-Nakhli
    Journal of Natural Gas Science and Engineering 83, 103606, (2020)
  • Chelating Agents as Acid-Fracturing Fluids: Experimental and Modeling Studies
    Z Tariq, MS Aljawad, A Hassan, M Mahmoud, A Al-Ramadhan
    Energy & Fuels 35 (3), 2602-2618, (2020)
  • Carbonate rocks resistivity determination using dual and triple porosity conductivity models
    Z Tariq, M Mahmoud, H Al-Youssef, MR Khan
    Petroleum 6 (1), 35-42, (2020)
  • Data-driven approaches to predict thermal maturity indices of organic matter using artificial neural networks
    Z Tariq, M Mahmoud, M Abouelresh, A Abdulraheem
    ACS omega 5 (40), 26169-26181, (2020)
  • Real-time prognosis of flowing bottom-hole pressure in a vertical well for a multiphase flow using computational intelligence techniques
    Z Tariq, M Mahmoud, A Abdulraheem
    Journal of Petroleum Exploration and Production Technology 10 (4), 1411-1428, (2020)
  • Core log integration: a hybrid intelligent data-driven solution to improve elastic parameter prediction
    Z Tariq, M Mahmoud, A Abdulraheem
    Neural Computing and Applications 31 (12), 8561-8581, (2019)

 

Education

  • Ph.D., Petroleum Engineering, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia, 2020
  • MS., Petroleum Engineering, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia, 2016
  • BE., Petroleum Engineering, NED University of Engineering and Technology, Karachi, Pakistan, 2012

Professional Profile

  • 2020 - 2021: Postdoctoral Research Fellow, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia.
  • 2014 - 2020: Graduate Research Assistant, Petroleum Engineering, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia.

Scientific and Professional Membership

  • Society of Petroleum Engineers (SPE)
  • American Rock Mechanics Association (ARMA)
  • Society of Petrophysics and Well Logs Association (SPWLA)
  • Pakistan Engineering Council (PEC)


Awards

  • 1st Prize in Middle East and North Africa Regional PetroBowl Championship
  • 3rd Prize in Middle East and North Africa Regional PetroBowl Championship
  • 3rd Prize in all Middle East and North Africa Regional Student Paper Contest

KAUST Affiliations

  • Ali I. Al-Naimi Petroleum Engineering Research Center (ANPERC)
  • Physical Science and Engineering Division (PSE)