Geoenergy in the hands of early career researchers - webinar

In the race to realise the transition to a sustainable energy future, early career scientists are paving the way. By repurposing existing methodologies for novel applications and developing entirely new techniques, research led by early career scientists is driving change.

This series of webinars will give a voice to early career researchers who recently published in Geoenergy’s thematic collection The sustainable future of geoenergy in the hands of early career researchers. Each one-hour session (with brief audience Q&A) focuses one or two papers on a wide range of topics spanning the energy transition.

Deep learning-assisted borehole image analysis for enhanced geothermal reservoir evaluation: a case study in the West Netherlands Basin

In this first webinar, we welcome Attilio Molossi, University of Trieste, who will present a deep learning–based workflow for automated fracture detection from borehole images to support geothermal reservoir evaluation. Using FMI data from the Naaldwijk well in the West Netherlands Basin, the study demonstrates how AI-assisted interpretation combined with expert validation can improve fracture characterization while reducing subjectivity in traditional manual analysis.

The published paper can be found at the Lyell Collection website.

This webinar will take place virtually, via Zoom

12:00 - 12:05 Introductions

12:05 - 12:35 Speaker

12:35 - 12:45 Q&A

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Chair - Pierre-Olivier Bruna, TU Delft

I am a geologist specialized in field structural geology (principally on carbonate rocks) and in 3D modelling / geostatistics. I obtained my PhD in geology in 2013 in Aix-Marseille University (France). Since 2013, he was recruited by the Northern Territory Geological Survey to investigate the potential of the greater McArthur Basin to host unconventional hydrocarbon resources. In late 2016, I joined the Geoscience and Engineering department of TU Delft to work with fracture network in carbonate reservoirs. My project attached to characterise fracture in outcrops and to use these data to predict the fracture network geometry and its efficiency in subsurface

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Attilio Molossi, University of Trieste

I am a geoscientist working at the intersection of geoscience and artificial intelligence, with a focus on applying machine learning and deep learning to subsurface and drilling data. I completed a PhD on deep learning applications to borehole imagery, contributing to research aimed at improving interpretation and reducing uncertainty in drilling operations. I am currently a Data Engineer at GEOLOG International, where I support drilling activities worldwide and develop data-driven solutions to address the challenges of complex and remote operational environments.

This webinar is free to attend. You can register by clicking the 'Book Now' button on the webpage.