ZOOM WEBINAR PRESENTATION
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Abstract: The talk introduces an innovative platform originally designed at Stanford University for the Stanford Total Enhanced Modeling of Source Rock (STEMS) project. The system described is an X-ray compatible, high-temperature (>400 °C), high-pressure (>2000 psi confining, >140 kN vertical load) triaxial core holder for a medical CT scanner allowing for the in-situ study of reactive flow mechanisms under effective stress conditions. We also employed a non-wetting technique to visualize and quantify sub-core scale porosity evolution with time as a result of kerogen pyrolysis and elucidated poroelasticity effects under triaxial loading conditions. The platform serves as a basis for the 2nd generation currently being developed at the CPG, KFUPM notably targeting axial loads of 1000 kN and confining pressures of 10,000 psi targeting carbon sequestration and EGS studies under true reservoir conditions. In addition, I will briefly touch on recent work on Deep Learning based methods to improve computed tomography scan quality.
Biography: Dr. Glatz received his Ph.D. and MSc degrees in Petroleum Engineering from Stanford University, California, USA, in 2017, and 2012, respectively. He holds a Dipl.-Ing. (FH) degree in Telematics from the Carinthia University of Applied Sciences, Austria, in 2004, and a BSc. in Petroleum Engineering from the Mining University of Leoben, Austria, in 2009. During his Ph.D., he developed a unique experimental platform for triaxial, high-pressure/high-temperature, testing of materials using computed tomography (CT). Dr. Glatz also worked for several national and international energy companies most notably as a technology consultant for the Chief Technology Office of British Petroleum (BP) and as a reservoir engineer consultant for Total S.A. His research focuses on visualizing and quantifying flow and transport phenomena under extreme temperature and pressure conditions in 4D. He also investigates the application of image processing and Deep Learning methods to improve CT data quality and allow for detection of phenomena on a sub-voxel level.