10-7 Yoon Seminar

Sustainable Education Building Room 122
Monday, October 7, 2019 - 15:00


Hongkyu Yoon, Ph.D.
Geomechanics Department
Sandia National Laboratories

Quantifying in-situ subsurface stresses and predicting fracture development are critical to reducing risks of induced seismicity and improving modern energy activities in the subsurface. In this work, we developed a novel integration of controlled mechanical failure experiments coupled with microCT imaging, acoustic sensing, modeling of fracture initiation and propagation, and machine learning for event detections and waveform characterization. Through additive manufacturing (3D printing), we were able to produce bassanite-gypsum rock samples with repeatable physical, geochemical and structural properties. With these “geo-architected” rock, we provided the role of mineral texture orientation on fracture surface roughness. The impact of poroelastic coupling on induced seismicity has been systematically investigated to improve mechanistic understanding of post shut-in surge of induced seismicity. This research will set the groundwork for characterizing seismic waveforms by using multiphysics and machine learning approaches and improve the detection of low-magnitude seismic events leading to the discovery of hidden fault/fracture systems. 


Dr. Hongkyu Yoon obtained a PhD in Environmental Engineering in Civil Engineering from the University of Illinois at Urbana-Champaign in 2005. After working as a visiting research assistant professor at UIUC, he joined the Geomechanics Department at Sandia in 2010 and currently a principal member of technical staff. He is an expert in hydrogeology and fluid mechanics, microfluidic and flow cell experiments, characterization of pore topology, chemo-mechanical processes in porous and fractured media, and high-fidelity inverse modeling, specializing in applications of coupled hydrogeological, geomechanical, and geochemical processes. His recent research focuses on induced seismicity during subsurface energy activities (e.g., CO2 sequestration, unconventional oil and gas recovery, and geothermal recovery), machine learning/deep learning applications for subsurface material characterization and multiscale simulations, coupled thermal-mechanical-hydrological-chemical processes with applications to subsurface energy technologies, and geomaterial (shale, carbonate rocks, salt, clay) characterizations with multiscale imaging and mechanical testing.  Recently he pioneered applying 3D printing techniques for fluid flow and mechanical behaviors on 3D digital rock structures and their applications for upscaling.