Sebastian Hoerning

Dr. Sebastian Hoerning is a Research Fellow at The University of Queensland's Gas & Energy Transition Research Centre within the Faculty of Engineering, Architecture and Information Technology. His work focuses on advanced geostatistical methods for energy transition and environmental applications.

His educational background includes:

• Bachelor of Science from Universität Stuttgart
• Masters (Coursework) of Science from Universität Stuttgart
• Doctor of Philosophy from Universität Stuttgart

Dr. Hoerning's research centers on geostatistics and stochastic modeling with applications in underground hydrogen storage, fugitive methane emissions, and reservoir characterization. His innovative work on copula-based non-linear geostatistics addresses directional asymmetry in spatial fields, providing more accurate representations of geological formations. He develops computational methods that bridge theoretical statistics with practical energy challenges, particularly relevant to Australia's energy transition.

His recent publications demonstrate a clear focus on critical energy transition issues, with particular emphasis on underground hydrogen storage, fugitive methane monitoring, and advanced computational geostatistics. These works show progression from fundamental methodological developments to applied solutions for real-world energy challenges.

Dr. Hoerning has secured significant research funding including:

• Novel statistical estimation of fugitive methane emissions using drones (Advance Queensland Industry Research Fellowships, 2022-2024)
• New Stratigraphy and Geostatistics for Gas and Water Resources (ARC Linkage Projects, 2020-2023)
• Impact of Geostatistical Techniques on Technically Recoverable Resources (Australian Energy Resource Growth Centre Ltd, 2018-2021)

He actively supervises PhD students, serving as Principal Advisor for research on hydrogen storage repurposing of gas fields and Associate Advisor for waveform inversion techniques. His development of computational tools like RMWSPy enables spatial simulation for environmental applications, supporting both academic research and industry practice.