Klemens Katterbauer
Papers
7
Total Citations
26
H-Index
3
About
Klemens Katterbauer is at the forefront of integrating artificial intelligence, deep learning, and 4th Industrial Revolution technologies into subsurface energy management. His research centers on reservoir monitoring, CO₂ injection optimization, and smart sensor deployment for enhanced oil recovery and carbon footprint reduction. Katterbauer’s most cited work introduces a novel deep reinforcement learning approach for sensor placement to track waterfront movement in fractured carbonate reservoirs, a critical challenge for maximizing sweep efficiency. He has also pioneered a deep learning framework for optimizing CO₂ injection, leveraging the gas’s solubility to swell oil and reduce viscosity for improved recovery. His real-time autoregressive deep learning system for automatic surface logging and intelligent sensor selection for subsurface CO₂ flow monitoring further demonstrate his commitment to automation and data-driven decision-making. With a growing body of work that includes minimizing carbon footprints through sustainable reservoir management and advancing wireless communication for subsurface sensors, Katterbauer’s contributions are shaping the future of smart, efficient, and environmentally conscious energy extraction.
Research Focus
Key Achievements
Top Papers
- 1A Novel Deep Reinforcement Sensor Placement Method for Waterfront Tracking13 citations · 2021
- 2A Deep Learning Wag Injection Method for Co2 Recovery Optimization4 citations · 2021
- 3
- 4
- 5Minimizing Carbon Footprint by Smart Sustainable Reservoir Management2 citations · 2021
- 6
- 7