# Robust representations of oil wells' intervals via sparse attention mechanism

arXiv: https://arxiv.org/abs/2212.14246 · HTML: https://mlsanity.com/paper/2212.14246/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 38 references that are in the map.
- First posted on arXiv 29 Dec 2022.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [Big Bird: Transformers for Longer Sequences](https://mlsanity.com/paper/2007.14062/) (2020)
- [Reformer: The Efficient Transformer](https://mlsanity.com/paper/2001.04451/) (2020)
- [Rethinking Attention with Performers](https://mlsanity.com/paper/2009.14794/) (2020)
- [Linformer: Self-Attention with Linear Complexity](https://mlsanity.com/paper/2006.04768/) (2020)
- [Attention is not Explanation](https://mlsanity.com/paper/1902.10186/) (2019)
- [Efficient Transformers: A Survey](https://mlsanity.com/paper/2009.06732/) (2020)
- [Long Range Arena: A Benchmark for Efficient Transformers](https://mlsanity.com/paper/2011.04006/) (2020)
- [Attention is not not Explanation](https://mlsanity.com/paper/1908.04626/) (2019)
- [Transformer Interpretability Beyond Attention Visualization](https://mlsanity.com/paper/2012.09838/) (2020)
- [Is Attention Interpretable?](https://mlsanity.com/paper/1906.03731/) (2019)
- [Random Feature Attention](https://mlsanity.com/paper/2103.02143/) (2021)
- [Self-Attention Attribution: Interpreting Information Interactions Inside Transformer](https://mlsanity.com/paper/2004.11207/) (2020)
- [Quadrature-based features for kernel approximation](https://mlsanity.com/paper/1802.03832/) (2018)
- [Input-Cell Attention Reduces Vanishing Saliency of Recurrent Neural Networks](https://mlsanity.com/paper/1910.12370/) (2019)
- [Acceptability Judgements via Examining the Topology of Attention Maps](https://mlsanity.com/paper/2205.09630/) (2022)
- [Similarity learning for wells based on logging data](https://mlsanity.com/paper/2202.05583/) (2022)
- [Development of Deep Transformer-Based Models for Long-Term Prediction of Transient Production of Oil Wells](https://mlsanity.com/paper/2110.06059/) (2021)
- [ScaleFace: Uncertainty-aware Deep Metric Learning](https://mlsanity.com/paper/2209.01880/) (2022)
- [Self-supervised similarity models based on well-logging data](https://mlsanity.com/paper/2209.12444/) (2022)

## Abstract (arXiv)

Transformer-based neural network architectures achieve state-of-the-art results in different domains, from natural language processing (NLP) to computer vision (CV). The key idea of Transformers, the attention mechanism, has already led to significant breakthroughs in many areas. The attention has found their implementation for time series data as well. However, due to the quadratic complexity of the attention calculation regarding input sequence length, the application of Transformers is limited by high resource demands. Moreover, their modifications for industrial time series need to be robust to missing or noised values, which complicates the expansion of the horizon of their application. To cope with these issues, we introduce the class of efficient Transformers named Regularized Transformers (Reguformers). We implement the regularization technique inspired by the dropout ideas to improve robustness and reduce computational expenses. The focus in our experiments is on oil&gas data, namely, well logs, a prominent example of multivariate time series. The goal is to solve the problems of similarity and representation learning for them. To evaluate our models for such problems, we work with an industry-scale open dataset consisting of well logs of more than 20 wells. The experiments show that all variations of Reguformers outperform the previously developed RNNs, classical Transformer model, and robust modifications of it like Informer and Performer in terms of well-intervals' classification and the quality of the obtained well-intervals' representations. Moreover, the sustainability to missing and incorrect data in our models exceeds that of others by a significant margin. The best result that the Reguformer achieves on well-interval similarity task is the mean PR~AUC score equal to 0.983, which is comparable to the classical Transformer and outperforms the previous models.