个人简历

Naser Golsanami is an associate professor in the College of Energy and Mining Engineering of Shandong University of Science and Technology. Naser received his Ph.D. from China University of Petroleum (East China) (UPC) in July 2018. He is the establisher of the “Scientific Research and Publication Center”of UPC. Naser is the winner ofthe “Talented Young Scientists Program” of the Ministry of Science and Technology of the People’s Republic of China. He has taken an active role in Geological and Petroleum Engineering projects funded by both governmental and private sectors of the industry, for which the total founding reaches up to $0.5M. While serving as the reviewer of more than 15 peer-reviewed SCI journals includingFUEL, International Journal of Energy Research, Energy & Fuels, Journal of Petroleum Science and Engineering,etc, his research andpublications have mainly focused on rock physics, data mining and artificial intelligence, digital rock technology, as well as formation evaluation via nuclear magnetic resonance technique. He has also been involved in rock physics studies of unconventional coalbed methane and shale gas reservoirs. Currently, Naser is engaged in characterizing the mechanical behavior as well as pore structural features of natural gas hydrate reservoirs using fractal theory, digital rock physics, as well as deep learning convolutional neural networks.
Ph.D. in Geological Resources and Geological Engineering from China University of Petroleum (East China), China, 2014-2018.
Master of Science in Petroleum Exploration Engineering from Sahand University of Technology (SUT), Iran, 2009-2011.
Bachelor of Science in Mining Exploration Engineering from Sahand University of Technology (SUT), Iran, 2005-2009.
Address:
No. 323, J4 Building, College of Energy and Mining Engineering, Qingdao Campus, Shandong University of Science and Technology (No. 575, Qianwan Gang Street, Huangdao District, Qingdao, Shandong, China, Zip code: 266590)

研究领域

Digital Rock Physics
Natural Gas Hydrates
Reservoir Geomechanics and Rock Mechanics
2D NMR
Machine Learning
Coalbed Methane""

近期论文

1. Golsanami, N., Sun, J., Liu, Y., Yan, W., Lianjun, C., Jiang, L., Dong, H., Zong, C., Wang, H., 2019. Distinguishing fractures from matrix pores based on the practical application of rock physics inversion and NMR data: A case study from an unconventional coal reservoir in China. J. Nat. Gas Sci. Eng. 65, 145–167.https://doi.org/10.1016/j.jngse.2019.03.006
2. Yan, W., Sun, J., Golsanami, N., Li, M., Cui, L., Dong, H., Sun, Y., 2019a. Evaluation of wettabilities and pores in tight oil reservoirs by a new experimental design. Fuel 252, 272–280.https://doi.org/10.1016/J.FUEL.2019.04.130
3. Golsanami, N., Sun, J., Zhang, Z., 2016. A review on the applications of the nuclear magnetic resonance (NMR) technology for investigating fractures. J. Appl. Geophys. 133, 30–38.https://doi.org/10.1016/j.jappgeo.2016.07.026
4. Golsanami, N., Kadkhodaie-Ilkhchi, A., Erfani, A., 2015. Synthesis of capillary pressure curves from post-stack seismic data with the use of intelligent estimators: A case study from the Iranian part of the South Pars gas field, Persian Gulf Basin. J. Appl. Geophys. 112, 215–225.https://doi.org/10.1016/j.jappgeo.2014.11.013
5. Golsanami, N., Kadkhodaie-Ilkhchi, A., Sharghi, Y., Zeinali, M., 2014. Estimating NMR T2 distribution data from well log data with the use of a committee machine approach: A case study from the Asmari formation in the Zagros Basin, Iran. J. Pet. Sci. Eng. 114, 38–51.https://doi.org/10.1016/j.petrol.2013.12.003