Modeling extra-deep electromagnetic logs using a deep neural network

Type of publication
Article in journal
Authors

Sergey Alyaev, Mostafa Shahriari, David Pardo, Ángel Javier Omella, David Selvåg Larsen, Nazanin Jahani, Erich Suter.


Abstract

Modern geosteering is heavily dependent on real-time interpretation of deep electromagnetic (EM) measurements. We have developed a methodology to construct a deep neural network (DNN) model trained to reproduce a full set of extra-deep EM logs consisting of 22 measurements per logging position. The model is trained in a 1D layered environment consisting of up to seven layers with different resistivity values. A commercial simulator provided by a tool vendor is used to generate a training data set. The data set size is limited because the simulator provided by the vendor is optimized for sequential execution. Therefore, we design a training data set that embraces the geologic rules and geosteering specifics supported by the forward model. We use this data set to produce an EM simulator based on a DNN without access to the proprietary information about the EM tool configuration or the original simulator source code. Despite using a relatively small training set size, the resulting DNN forward model is quite accurate for the considered examples: a multilayer synthetic case and a section of a published historical operation from the Goliat field. The observed average evaluation time of 0.15 ms per logging position makes it also suitable for future use as part of evaluation-hungry statistical and/or Monte Carlo inversion algorithms within geosteering workflows.

Conference / Journal
Geophysics
Publisher
SEG Library
Year of publication
2021
Citation

Modeling extra-deep electromagnetic logs using a deep neural network, Sergey Alyaev, Mostafa Shahriari, David Pardo, Ángel Javier Omella, David Selvåg Larsen, Nazanin Jahani, and Erich Suter, GEOPHYSICS 2021 86:3, E269-E281, https://doi.org/10.1190/geo2020-0389.1

DOI
https://doi.org/10.1190/geo2020-0389.1