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PhD Student

   Research focus: 

Experimental Study of multiphase flow in porous media, CO2 reactive flow in porous media at pore and core scale.

Contact Information

Office: JN1.29

Phone: +44 (0) 131 451 3809

Email: sg116@hw.ac.uk

 

 Biography:


Shima holds a BSc in Chemical Engineering from Petroleum University of Technology, Iran.  In 2015, she joined Hydrafact Ltd as a Flow Assurance Research Scientist, where she was mainly involved with corrosion studies.  She is currently a PhD student under the supervision of Prof. Mercedes Maroto-Valer and involved in the MILEPOST project, where her research study will focus on the unravelling of the CO2 reactive flow drivers at multi-scale (pore to core) during the CO2 sequestration process. 

 

Research Interests:


Her main research areas are CO2 reactive flow in porous media at pore and core scale and providing new experimental data to understand the main drivers of CO2 sequestration.  

 

Recent Publications:


  1. Sh. Ghanaatian, E. Joonaki, “Application of Nanofluids for Enhanced Oil Recovery: Effects on Interfacial Tension and Coreflooding Process”, Journal of Petroleum Science and Technology, Volume 32, Issue 21, Pages 2599-2607, 2014.
  2. Sh. Ghanaatian, E. Joonaki, H. Erfani, “Experimental Study on Adsorption and Wettability Alteration Aspects of a New Surfactant on Carbonate Reservoir Rocks”, Journal of Unconventional Oil and Gas Resources, Volume 15, September 2016, Pages 11–21.
  3. E. Joonaki, Sh. Ghanaatian, Gh. Zargar, ‘A New Approach to Simultaneously Enhancing Heavy Oil Recovery and Hindering Asphaltene Precipitation’, Iranian Journal of Oil & Gas Science and Technology, Vol. 1 (2012), No. 1, pp. 37-42. 
  4. E. Joonaki, Sh. Ghanaatian, Gh. Zargar, ‘An Intelligence Approach for Porosity and Permeability Prediction of Oil Reservoirs using Seismic Data’, International Journal of Computer Applications 80(8):19-26, October 2013. 
  5. A. Rostami, M. Arabloo, E. Joonaki, Sh. Ghanaatian, A. Hassanpouryouzband, “Fast Estimation of Supercritical CO2 Thermal Conductivity by a Supervised Learning Machine: Implications for EOR”, 79th EAGE Conference and Exhibition 2017 (EUROPEC), Paris, France. 

 


 

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