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Speaker: Paolo Dell'Aversana, eni E&P

Eni S.p.A. Reinforcement Learning (RL) includes a suite of algorithms and techniques through which an “artificial agent” learns an optimal “policy” by interacting with a dynamic “environment” and by maximizing a “reward metric” for the task, without being explicitly programmed for that task and without human intervention. In this webinar, the speaker will introduce briefly some of the main RL techniques and algorithms, with particular focus on applications in geosciences and field management. He will show how algorithms like Q-Learning, supported by Deep Neural Networks, can support the human decisional process for optimizing geophysical data inversion, hydrocarbon production and water resources management. 

Speaker Biography: Paolo Dell'Aversana, eni E&P
Paolo Dell’Aversana graduated in Geology in 1988 and in Physics in 1996, at Naples University. From 1997 to 1999 he studied Philosophy of Science and Aesthetics at University of Rome “La Sapienza”.He started his carrier in 1988, in a private company of geophysical services were he has been employed for about five years. Then he worked as temporary researcher at the Department of Geophysics and Volcanology at Naples University.From 1996 to 2002 he worked at Enterprise Oil Italiana, in seismic acquisition, processing and travel time tomography. He was responsible also for acquisition, modelling and inversion of magnetotelluric and gravity data.

Since 2002 he is technical leader and senior researcher in eni E&P. His main responsibility is to develop and to apply integrated approaches in geophysics, using electromagnetic methods combined with seismic and gravity. Over the past decade he has been project manager of many projects of electromagnetic data acquisition, modelling, inversion and interpretation. He is expert in Data Science and Machine Learning, in joint inversion of multi-disciplinary geophysical data, in borehole electromagnetic, in rock physics and in seismic tomography.