# Modeling extra-deep electromagnetic logs using a deep neural network

arXiv: https://arxiv.org/abs/2005.08919 · HTML: https://mlsanity.com/paper/2005.08919/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2020.

## Built on by (most cited first, 6 of 6)

- [Probabilistic model-error assessment of deep learning proxies: an application to real-time inversion of borehole electromagnetic measurements](https://mlsanity.com/paper/2205.12684/) (2022)
- [r-Adaptive Deep Learning Method for Solving Partial Differential Equations](https://mlsanity.com/paper/2210.10900/) (2022)
- [Probabilistic forecasting for geosteering in fluvial successions using a generative adversarial network](https://mlsanity.com/paper/2207.01374/) (2022)
- [Strategic Geosteering Workflow with Uncertainty Quantification and Deep Learning: A Case Study on the Goliat Field](https://mlsanity.com/paper/2210.15548/) (2022)
- [A Generative-AI Modeling Framework for Explainable Decision Support in Complex Geosteering Scenarios](https://mlsanity.com/paper/2503.08509/) (2025)
- [Automated machine learning for borehole resistivity measurements](https://mlsanity.com/paper/2207.09849/) (2022)

## Abstract (arXiv)

Modern geosteering is heavily dependent on real-time interpretation of deep electromagnetic (EM) measurements. We present 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 dataset. The dataset size is limited because the simulator provided by the vendor is optimized for sequential execution. Therefore, we design a training dataset that embraces the geological rules and geosteering specifics supported by the forward model. We use this dataset 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 employing a relatively small training set size, the resulting DNN forward model is quite accurate for the considered examples: a multi-layer 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.