# A Deep Learning Approach to the Inversion of Borehole Resistivity Measurements

arXiv: https://arxiv.org/abs/1810.04522 · HTML: https://mlsanity.com/paper/1810.04522/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2018.

## Built on by (most cited first, 7 of 7)

- [Physics Embedded Machine Learning for Electromagnetic Data Imaging](https://mlsanity.com/paper/2207.12607/) (2022)
- [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)
- [Inversion of Time-Lapse Surface Gravity Data for Detection of 3D CO2 Plumes via Deep Learning](https://mlsanity.com/paper/2209.02850/) (2022)
- [High-Precision Geosteering via Reinforcement Learning and Particle Filters](https://mlsanity.com/paper/2402.06377/) (2024)
- [Joint inversion of Time-Lapse Surface Gravity and Seismic Data for Monitoring of 3D CO2 Plumes via Deep Learning](https://mlsanity.com/paper/2310.04430/) (2023)
- [Design of borehole resistivity measurement acquisition systems using deep learning](https://mlsanity.com/paper/2101.05623/) (2021)
- [Automated machine learning for borehole resistivity measurements](https://mlsanity.com/paper/2207.09849/) (2022)

## Abstract (arXiv)

We use borehole resistivity measurements to map the electrical properties of the subsurface and to increase the productivity of a reservoir. When used for geosteering purposes, it becomes essential to invert them in real time. In this work, we explore the possibility of using Deep Neural Network (DNN) to perform a rapid inversion of borehole resistivity measurements. Herein, we build a DNN that approximates the following inverse problem: given a set of borehole resistivity measurements, the DNN is designed to deliver a physically meaningful and data-consistent piecewise one-dimensional layered model of the surrounding subsurface. Once the DNN is built, we can perform the actual inversion of the field measurements in real time. We illustrate the performance of DNN of logging-while-drilling measurements acquired on high-angle wells via synthetic data.