A deep neural network as surrogate model for forward simulation of borehole resistivity measurements

Autoren Mostafa Shariari Shourabi
David Pardo
Bernhard A. Moser
Florian Sobieczky
Editoren
Titel A deep neural network as surrogate model for forward simulation of borehole resistivity measurements
Typ Artikel
Journal Procedia Manufacturing
Band 42
DOI 10.1016/j.promfg.2020.02.075
Monat March
Jahr 2020
Seiten 235-238
SCCH ID# 19080
Abstract

Inverse problems appear in multiple industrial applications. Solving such inverse problems require the repeated solution of the forward problem. This is the most time-consuming stage when employing inversion techniques, and it constitutes a severe limitation when the inversion needs to be performed in real-time. In here, we focus on the real-time inversion of resistivity measurements for geosteering. We investigate the use of a deep neural network (DNN) to approximate the forward function arising from Maxwell’s equations, which govern the electromagnetic wave propagation through a media. By doing so, the evaluation of the forward problems is performed offline, allowing for the online real-time evaluation
(inversion) of the DNN.