Name: Xinmin Ge
Academic title: Professor
Research Area:
Petrophysics
NMR logging theory and data processing
Complex and unconventional reservoir logging evaluation
Artificial intelligence, machine learning and applications
Contact:
Email: gexinmin2002@upc.edu.cn
Research Interests:
Petrophysics, low field NMR, Formation evaluation, Machine learning in Geosciences
Courses Offered：
Petrophysics, Geophysical Logging, Special Logging Data Processing and Application, New Logging Technology Application Training, Logging digital Processing and Comprehensive interpretation.
Publications：
[1].Ge, X., Fan, Y., Liu, J., Xing, D., Xu, H. and Hu, F., 2023. An Empirical Method to Correct NMR Porosity of Tight Sandstone Using Low Field Nuclear Magnetic Resonance Data. AAPG Bulletin, 107 (4), pp539–551.
[2].Zhao, J., Ge, X., Fan, Y., Liu, J., Chen, Y. and Xing, L., 2023. A genetic algorithm–driven support vector machine to discriminate the kerogen type using conventional geophysical logging data. AAPG Bulletin, 107(11), pp.1837-1849.
[3].Ge, X., Zhang, R., Liu, J., Fan, Y., Zhao, J., Li, C. and Hu, F., 2022. NMR transverse relaxation of the clay-rich shale in inhomogeneous magnetic field: A numerical study. Computers & Geosciences, 166, p.105174.
[4].Ge, X., Fan, Y., Liu, J., Zhao, J., Zeng, B. and Xing, D., 2021. Numerical investigating the low field NMR response of representative pores at different pulse sequence parameters. Computers & Geosciences, 151, p.104761.
[5].Ge, X., Myers, M.T., Liu, J., Fan, Y., Zahid, M.A., Zhao, J. and Hathon, L., 2021. Determining the transverse surface relaxivity of reservoir rocks: A critical review and perspective. Marine and Petroleum Geology, 126, p.104934.
[6].Ge, X., Xiao, Y., Fan, Y., Liu, J. and Zhang, Y., 2020. Laboratory investigation of the relationship between static rock elastic parameters and low field nuclear magnetic resonance data. International Journal of Rock Mechanics and Mining Sciences, 127, p.104207.
[7].Ge, X., Zhao, J., Zhang, F., Fan, Y., Liu, J., Cai, J., Nie, S. and Wang, C., 2019. A practical method to compensate for the effect of echo spacing on the shale NMR T2 spectrum. Earth and Space Science, 6(8), pp.1489-1497.
[8].Sheng, C., Wenzhi, Z., Xinmin, G., Qingcai, Z., Qing, Y. and Shaohua, G., 2019. Predicting gas content in high-maturity marine shales using artificial intelligence based seismic multiple-attributes analysis: A case study from the lower Silurian Longmaxi Formation, Sichuan Basin, China. Marine and Petroleum Geology, 101, pp.180-194.
[9].Ge, X., Liu, J., Fan, Y., Xing, D., Deng, S. and Cai, J., 2018. Laboratory investigation into the formation and dissociation process of gas hydrate by low‐field NMR technique. Journal of Geophysical Research: Solid Earth, 123(5), pp.3339-3346.
Research Project：
[1].National Natural Science Foundation of China (No42174142).
[2].CNPC Innovation Fund (No. 2021DQ02-0402).
