# Deep learning for prediction of complex geology ahead of drilling

arXiv: https://arxiv.org/abs/2104.02550 · HTML: https://mlsanity.com/paper/2104.02550/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 33 references that are in the map.
- First posted on arXiv 6 Apr 2021.

## Builds on (most cited first, 4 of 4)

- [Wasserstein GAN](https://mlsanity.com/paper/1701.07875/) (2017)
- [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)
- [Parametrization of stochastic inputs using generative adversarial networks with application in geology](https://mlsanity.com/paper/1904.03677/) (2019)

## Built on by (most cited first, 1 of 1)

- [Probabilistic forecasting for geosteering in fluvial successions using a generative adversarial network](https://mlsanity.com/paper/2207.01374/) (2022)

## Abstract (arXiv)

During a geosteering operation the well path is intentionally adjusted in response to the new data acquired while drilling. To achieve consistent high-quality decisions, especially when drilling in complex environments, decision support systems can help cope with high volumes of data and interpretation complexities. They can assimilate the real-time measurements into a probabilistic earth model and use the updated model for decision recommendations. Recently, machine learning (ML) techniques have enabled a wide range of methods that redistribute computational cost from on-line to off-line calculations. In this paper, we introduce two ML techniques into the geosteering decision support framework. Firstly, a complex earth model representation is generated using a Generative Adversarial Network (GAN). Secondly, a commercial extra-deep electromagnetic simulator is represented using a Forward Deep Neural Network (FDNN). The numerical experiments demonstrate that the combination of the GAN and the FDNN in an ensemble randomized maximum likelihood data assimilation scheme provides real-time estimates of complex geological uncertainty. This yields reduction of geological uncertainty ahead of the drill-bit from the measurements gathered behind and around the well bore.