# Probabilistic forecasting for geosteering in fluvial successions using a generative adversarial network

arXiv: https://arxiv.org/abs/2207.01374 · HTML: https://mlsanity.com/paper/2207.01374/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 4 Jul 2022.

## Builds on (most cited first, 6 of 6)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Parametric generation of conditional geological realizations using generative neural networks](https://mlsanity.com/paper/1807.05207/) (2018)
- [A Decision Support System for Multi-target Geosteering](https://mlsanity.com/paper/1903.03933/) (2019)
- [Modeling extra-deep electromagnetic logs using a deep neural network](https://mlsanity.com/paper/2005.08919/) (2020)
- [Deep learning for prediction of complex geology ahead of drilling](https://mlsanity.com/paper/2104.02550/) (2021)

## Built on by (most cited first, 2 of 2)

- [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)

## Abstract (arXiv)

Quantitative workflows utilizing real-time data to constrain ahead-of-bit uncertainty have the potential to improve geosteering significantly. Fast updates based on real-time data are essential when drilling in complex reservoirs with high uncertainties in pre-drill models. However, practical assimilation of real-time data requires effective geological modeling and mathematically robust parameterization. We propose a generative adversarial deep neural network (GAN), trained to reproduce geologically consistent 2D sections of fluvial successions. Offline training produces a fast GAN-based approximation of complex geology parameterized as a 60-dimensional model vector with standard Gaussian distribution of each component. Probabilistic forecasts are generated using an ensemble of equiprobable model vector realizations. A forward-modeling sequence, including a GAN, converts the initial (prior) ensemble of realizations into EM log predictions. An ensemble smoother minimizes statistical misfits between predictions and real-time data, yielding an update of model vectors and reduced uncertainty around the well. Updates can be then translated to probabilistic predictions of facies and resistivities. The present paper demonstrates a workflow for geosteering in an outcrop-based, synthetic fluvial succession. In our example, the method reduces uncertainty and correctly predicts most major geological features up to 500 meters ahead of drill-bit.