Machine learning for modeling and interpreting geophysical borehole measurements (David Pardo, University of the Basque Country)


David Pardo from University of the Basque Country


Mahdi Abedi, Mostafa Shahriari, Ali Hashemian, Jon A. Rivera, and Angel J. Omella


We propose using neural networks to solve borehole resistivity inverse problems for geosteering. While doing so, we face challenges like generating a massive database with forward problem solutions. The presentation covers: (a) the fundamentals of solving inverse problems with neural networks, (b) some of the main issues we encountered while trying to invert borehole resistivity measurements using neural networks, and (c) some proposed solutions and current limitations in the field.


David Pardo is a Research Professor at Ikerbasque, the University of the Basque Country UPV/EHU, and the Basque Center for Applied Mathematics (BCAM). He has published over 160 research articles and he has given over 260 presentations. In 2011, he was awarded as the best Spanish young researcher in Applied Mathematics by the Spanish Society of Applied Mathematics (SEMA). He leads a European Project on subsurface visualization, several national research projects, as well as research contracts with national and international companies. He is now the PI of the research group on Applied Mathematical Modeling, Statistics, and Optimization (MATHMODE) at UPV/EHU and co-PI of the sister research group at BCAM on Mathematical Design, Modeling, and Simulations (MATHDES). His research interests include computational electromagnetics, petroleum-engineering applications (borehole simulations), adaptive finite-element and discontinuous Petrov-Galerkin methods, multigrid solvers, deep learning algorithms, and multiphysics and inverse problems.

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