# Self-supervised similarity models based on well-logging data

arXiv: https://arxiv.org/abs/2209.12444 · HTML: https://mlsanity.com/paper/2209.12444/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 26 Sep 2022.

## Builds on (most cited first, 10 of 10)

- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Self-supervised Learning: Generative or Contrastive](https://mlsanity.com/paper/2006.08218/) (2020)
- [Tutorial on Variational Autoencoders](https://mlsanity.com/paper/1606.05908/) (2016)
- [MADE: Masked Autoencoder for Distribution Estimation](https://mlsanity.com/paper/1502.03509/) (2015)
- [Explicit Inductive Bias for Transfer Learning with Convolutional Networks](https://mlsanity.com/paper/1802.01483/) (2018)
- [DELTA: DEep Learning Transfer using Feature Map with Attention for Convolutional Networks](https://mlsanity.com/paper/1901.09229/) (2019)
- [Similarity learning for wells based on logging data](https://mlsanity.com/paper/2202.05583/) (2022)
- [Data-driven model for the identification of the rock type at a drilling bit](https://mlsanity.com/paper/1806.03218/) (2018)
- [Towards Better Shale Gas Production Forecasting Using Transfer Learning](https://mlsanity.com/paper/2106.11051/) (2021)
- [Development of Deep Transformer-Based Models for Long-Term Prediction of Transient Production of Oil Wells](https://mlsanity.com/paper/2110.06059/) (2021)

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

- [Non-contrastive representation learning for intervals from well logs](https://mlsanity.com/paper/2209.14750/) (2022)
- [Robust representations of oil wells' intervals via sparse attention mechanism](https://mlsanity.com/paper/2212.14246/) (2022)

## Abstract (arXiv)

Adopting data-based approaches leads to model improvement in numerous Oil&Gas logging data processing problems. These improvements become even more sound due to new capabilities provided by deep learning. However, usage of deep learning is limited to areas where researchers possess large amounts of high-quality data. We present an approach that provides universal data representations suitable for solutions to different problems for different oil fields with little additional data. Our approach relies on the self-supervised methodology for sequential logging data for intervals from well, so it also doesn't require labelled data from the start. For validation purposes of the received representations, we consider classification and clusterization problems. We as well consider the transfer learning scenario. We found out that using the variational autoencoder leads to the most reliable and accurate models. approach We also found that a researcher only needs a tiny separate data set for the target oil field to solve a specific problem on top of universal representations.