Ge Jin

Ge Jin

Assistant Professor
Co-director of Reservoir Characterization Project
Department of Geophysics

Short Bio

Dr. Ge Jin is Assistant Professor of Geophysics and co-PI of Reservoir Characterization Project at Colorado School of Mines. His research focuses on Distributed Fiber-Optic Sensing (DFOS) applications in the fields of oil & gas, geothermal, CO2 sequestration, smart city, and earthquake hazard. He is also interested in machine-learning applications and seismic imaging. He obtained his Ph.D. in Geophysics from Columbia University in the City of New York, and dual B.S. in Geophysics and Computer Science from Peking University in Beijing. He worked as a research geophysicist in the oil industry for five years before joining Colorado School of Mines as a faculty member in 2019.

Education

• B.S. in Geophysics, Peking University, 2006
• B.S. in Computer Science, Peking University, 2006
• M.S. in Geophysics, Peking University, 2009
• Ph.D. in Geophysics, Columbia University, 2014

Research

• Distributed Fiber Optic Sensing Applications
• Machine learning
• Seismic imaging and interpretation

Teaching

• GPGN319: Applied Geophysics II
  • 16-week in-person course
  • Syllabus

• GPGN436/536: Geophysical Computing/Advanced Geophysical Computing
  • Offered in fall semesters
  • Syllabus: GPGN436, GPGN536

• GPGN545: Introduction to Distributed Fiber-Optic Sensing and Its Applications
  • 8-week online course
  • Offered in fall semesters
  • Syllabus

Selected Publications

Mjehovich, J., Jin, G., Martin, E.R. and Shragge, J., 2023. Rapid Surface Deployment of a DAS System for Earthquake Hazard Assessment. Journal of Geotechnical and Geoenvironmental Engineering, 149(5), p.04023027.

Ning, Y. and Jin, G., 2023. Challenges and Best Practices in Interpreting Cross-well Strain Signals to Monitor Multi-Crew Zipper Fracturing Operations. Interpretation, 11(2), pp.1-43.

Zhu, X. and Jin, G., 2022. Hydraulic Fracture Propagation in Denver-Julesburg Basin Constrained by Cross-Well Distributed Strain Measurements. SPE Journal, 27(06), pp.3446-3454.

Titov, A., Jin, G., Binder, G. and Tura, A., 2022. Distributed acoustic sensing time-lapse vertical seismic profiling during zipper-fracturing operations: Observations, modeling, and interpretation. Geophysics, 87(6), pp.B329-B336.

Staněk, F., Jin, G. and Simmons, J., 2022. Fracture Imaging Using DAS-Recorded Microseismic Events. Frontiers in Earth Science, 10.

Liu, Y., Jin, G., Wu, K. and Moridis, G., 2022. Quantitative Hydraulic-Fracture-Geometry Characterization with Low-Frequency Distributed-Acoustic-Sensing Strain Data: Fracture-Height Sensitivity and Field Applications. SPE Production & Operations, 37(02), pp.159-168.

Schumann, H. and Jin, G., 2022. Inferring hydraulic connectivity of induced fractures in the near-wellbore region using distributed acoustic sensing-recorded tube waves excited by perforation shots. Geophysics, 87(3), pp.D101-D109.

Titov, A., Fan, Y., Kutun, K. and Jin, G., 2022. Distributed Acoustic Sensing (DAS) Response of Rising Taylor Bubbles in Slug Flow. Sensors, 22(3), p.1266.

Luo B., Jin, G., and Stanek, F., 2021. Near-field strain in DAS-based microseismic observation. Geophysics, 86(5), pp.1-49.

Jin, G., Ugueto, G., Wojtaszek, M., Guzik, A., Jurick, D. and Kishida, K., 2021. Novel Near-Wellbore Fracture Diagnosis for Unconventional Wells Using High-Resolution Distributed Strain Sensing during Production. SPE Journal, pp.1-10.

Luo, B., Lellouch, A., Jin, G., Biondi, B. and Simmons, J., 2021. Seismic inversion of shale reservoir properties using microseismic-induced guided waves recorded by distributed acoustic sensing (DAS). Geophysics, 86(4), pp.1-58.

Liu, Y., Jin, G., Wu, K., & Moridis, G., 2020, November. Hydraulic-Fracture-Width Inversion Using Low-Frequency Distributed-Acoustic-Sensing Strain Data – Part I: Algorithm and Sensitivity Analysis. Society of Petroleum Engineers. doi:10.2118/204225-PA

Titov, A., Binder, G., Liu, Y., Jin, G., Simmons, J., Tura, A., Monk, D., Byerley, G. and Yates, M., 2020. Modeling and interpretation of scattered waves in inter-stage DAS VSP survey. Geophysics, 86(2), pp.1-49.

Jin*, G., Friehauf*, K., Roy*, B., Constantine, J.J., Swan, H.W., Krueger, K.R. and Raterman, K.T., 2019, October. Fiber Optic Sensing-Based Production Logging Methods for Low-Rate Oil Producers. In Unconventional Resources Technology Conference, Denver, Colorado, 22-24 July 2019 (pp. 1183-1199). Unconventional Resources Technology Conference (URTeC); Society of Exploration Geophysicists.

Jin, G., Mendoza, K., Roy, B. and Buswell, D.G., 2019. Machine learning-based fracture-hit detection algorithm using LFDAS signal. The Leading Edge, 38(7), pp.520-524.

Jin, G. and Roy, B., 2017. Hydraulic-fracture geometry characterization using low-frequency DAS signal. The Leading Edge, 36(12), pp.975-980.

Jin, G. and Gaherty, J.B., 2015. Surface wave phase-velocity tomography based on multichannel cross-correlation. Geophysical Journal International, 201(3), pp.1383-1398.