EAGE London: Integrated Geophysics and Machine Learning

EAGE Local Chapter London

WEBINAR: Integrated Geophysics and Machine Learning for Risk Mitigation in Exploration Geosciences

<p>The talk will happen online - link to the webinar will be provided via e-mails to registered attendees: first e-mail will be sent <strong>two days before the event</strong> and the second one just <strong>2 hours before the event</strong>. Do not register too late!</p><h3></h3><h3>Agenda (UK time)</h3><p><strong>18:30-18:40</strong> Introduction and Announcements</p><p><strong>18:40-19:20</strong> Integrated Geophysics and Machine Learning for Risk Mitigation in Exploration Geosciences</p><p><strong>19:20-19:40</strong> Q&A </p><p><strong>19:40-21:00</strong> Informal discussion and relaxed networking </p><p>                                  (cameras + microphones + drinks ON)</p><h3></h3><h3>Speaker</h3><h3>Paolo Dell’Aversana, Eni S.p.A.</h3><p><strong>Paolo Dell’Aversana</strong> graduated in Geological Sciences (1988) and in Physics (1996). He has more than three decades of experience in various areas of the Earth disciplines, including geology, volcanology and exploration geophysics. He currently works in Eni S.p.A. as a senior geophysicist and project manager, for the development of innovative geophysical technologies and machine learning methods. He is the author of various patents, has published over one hundred specialist articles and several books. He has received international awards, including the prestigious Eni Award from the President of the Italian Republic, as a recognition for innovation.</p><h3>Abstract</h3><p>Machine Learning algorithms can support the integration workflow of multi-physics data sets in the process of exploration risk evaluation and/or in the process of field appraisal. The data set discussed in this talk includes seismic, electromagnetic (Marine Controlled Source Electromagnetics), gravity and borehole measurements. The integration approach is based on sequential geophysical modelling and constrained-cooperative inversion, combined with a suite of Machine Learning algorithms (Deep Neural Networks as well as other methods). Real case histories are discussed to show the effectiveness of this multi-physics data integration methodology.</p>

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EAGE London: Integrated Geophysics and Machine Learning

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