Qamar Yasin
Associate Professor
Cell: +8618546595033
E-mail: qyasin@nepu.edu.cn
LinkedIn: www.linkedin.com/in/qamar-yasin-phd
Research interests:
1. Geomechanical characterization of shale gas reservoirs
2. Seismic modelling of geothermal and fractured carbonate reservoirs
3. Well & core log data analysis, interpretation, and modeling
4. Seismic data interpretation, attributes, and inversion
5. Data science and machine learning for geosciences
Degrees:
2022 Associate Degree in Data Science and Machine Learning, Dice Analytics, Pakistan
2018 Ph.D. Geological Engineering (Geophysics), China University of Petroleum (East China)
2014 M. Phil. Applied Geology, University of the Punjab Lahore, Pakistan
2012 MBA HRM, Federal Urdu University of Arts, Science, & Technology, Islamabad, Pakistan
Current Position:
2022–present Associate Professor (full time), Northeast Petroleum University Daqing
Research and Professional Experience
2021–2022 Assistant Professor, Polish Academy of Sciences, Warsaw, Poland
2021–2021 AI/ML Associate Researcher, XpertFlow Technologies PTE. Ltd. Singapore
2018–2021 Postdoctoral Research Fellow, State key Laboratory of Deep Oil & Gas, UPC China
2007–2012 Senior Staff Geophysicist, Techno Group of Companies, UAE
2006–2007 Quality Control Engineer, Nativus Resources Limited (Oil & Gas British Company)
2005–2006 Assistant Geophysicist, Integrated Petroleum Consultant (Pvt) Ltd. Pakistan
Scholarly and professional activities
Associate Editor of “Acta Geophysica” (Springer-Nature) and Frontiers in Earth Science (frontiers).
Associate Editor of “Journal of GeoEnergy” (Hindawi)
Ongoing large research projects
2022–2023 Research Scientist (part time), Shandong University of Science and Technology, China
PI in research project ‘Seismic velocity analysis and rock physics modeling for temperature variation in structurally complex geothermal reservoirs’ awarded by National Natural Science Foundation of China (NSFC).
Past projects:
1. Co (PI) in research project ‘Seismic petrophysical analysis and identification parameters of Chengbei 30 Lower Paleozoic buried hill reservoir’ (2018-2019). Awarded by offshore oil production plant of Shengli oilfield branch China.
2. Co (PI) in research project ‘Study on seismic prediction technology of carbonate reservoir in offshore Chengbei 30 buried hill’ (2019-2020). Awarded by offshore oil production plant of Shengli oilfield branch China.
Selected Publications:
1. Liu, B., Wang, Y., Tian, S., Guo, Y., Wang, L., Yasin, Q., Yang, J. (2022). Impact of thermal maturity on the diagenesis and porosity of lacustrine oil-prone shales: Insights from natural shale samples with thermal maturation in the oil generation window. International Journal of Coal Geology. https://doi.org/10.1016/j.coal.2022.104079
2. Yasin, Q., Majdanski, M. Awan, R.S. Golsanami, N. (2022). An analytical hierarchy-based method for quantifying hydraulic fracturing stimulation to improve geothermal well productivity. Energies https://doi.org/10.3390/en15197368
3. Yasin, Q., Majdański, M. (2022). Fault and fracture network characterization using seismic data: A study based on neural network models assessment. Geomech. & Geophy. for Geo-Energy & Geo-Res. https://doi.org/10.1007/s40948-022-00352-y
4. Majid S., Hung Vo Thanh, Danial S., Yasin. Q. (2022). Application of robust intelligent schemes for accurate modelling interfacial tension of CO2 brine systems: implications for structural CO2 trapping. FUEL. https://doi.org/10.1016/j.fuel.2022.123821
5. Golsanami, N., Jayasuriya, MN., and Yasin, Q. (2021). Characterizing clay textures and their impact on the reservoir using deep learning and Lattice-Boltzmann simulation applied to SEM images. Energy. https://doi.org/10.1016/j.energy.2021.122599
6. Yasin, Q., Syrine, B., Du, Q. (2021). An integrated fracture parameter prediction and characterization method in deeply-buried carbonate reservoirs based on deep neural network. Journal of Petroleum Science and Engineering. https://doi.org/10.1016/j.petrol.2021.109346
7. Yasin, Q., Du, Q. (2021). Study of brittleness templates for longmaxi shale gas reservoir, Sichuan Basin China. Petroleum Sciences. https://doi.org/10.1016/j.petsci.2021.09.030
8. Yasin, Q., Yan D., Ismail, A. Du, Q. (2020). Estimation of petrophysical parameters from seismic inversion by combining particle swarm optimization and multilayer linear calculator. Natural Resources Research. https://doi.org/10.1007/s11053-020-09641-3
9. Yasin, Q., Syrine, B., Du, Q. (2020). Evaluation of shale-gas reservoirs in complex structural enclosures: A case study from Patala shale in Kohat-Potwar Plateau. Journal of Petroleum Science and Engineering. https://doi.org/10.1016/j.petrol.2020.108225
10. Yasin, Q., Khalid, P., Du, Q. (2020). Application of machine learning tool to predict the porosity of clastic depositional system, Indus Basin, Pakistan. Journal of Petroleum Science and Engineering. https://doi.org/10.1016/j.petrol.2020.107975
11. Yasin, Q., Sohail, GMD. (2022). Evaluation of Neoproterozoic reservoirs in SE Pakistan and adjacent Bikaner-Nagaur Basin India based on integrated geochemical, geological, and geophysical data. Scientific Reports. https://doi.org/10.1038/s41598-022-14831-5
12. Yasin, Q., Du, Q. and Ismail, A. (2019). A new integrated workflow for improving permeability estimation in a highly heterogeneous reservoir of Sawan Gas Field. Geomechanics & Geophysics for Geo-Energy & Geo-Resources. https://doi.org/10.1007/s40948-018-0101-y
13. Yan, D., Yasin, Q., Cui, M., (2021). A novel neural network for seismic anisotropy and fracture porosity measurements in carbonate reservoirs. Arabian Journal for Science and Engineering. https://doi.org/10.1007/s13369-021-05970-4
14. Hung Vo Thanh, Yasin, Q., (2022). Knowledge-based machine learning techniques for accurate prediction of CO2 storage performance in underground saline aquifers. Applied Energy. https://doi.org/10.1016/j.apenergy.2022.118985
15. Du, Q., Yasin, Q. (2019). Combining classification and regression for improving shear wave velocity estimation in a highly heterogeneous reservoir from well logs data. Journal of Petroleum Science and Engineering. https://doi.org/10.1016/j.petrol.2019.106260
