# Similarity learning for wells based on logging data

arXiv: https://arxiv.org/abs/2202.05583 · HTML: https://mlsanity.com/paper/2202.05583/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 50 references that are in the map.
- First posted on arXiv 11 Feb 2022.

## Builds on (most cited first, 18 of 18)

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- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [Deep Visual Domain Adaptation: A Survey](https://mlsanity.com/paper/1802.03601/) (2018)
- [A Survey on Contrastive Self-supervised Learning](https://mlsanity.com/paper/2011.00362/) (2020)
- [Time-Series Representation Learning via Temporal and Contextual Contrasting](https://mlsanity.com/paper/2106.14112/) (2021)
- [LSTM: A Search Space Odyssey](https://mlsanity.com/paper/1503.04069/) (2015)
- [CatBoost: gradient boosting with categorical features support](https://mlsanity.com/paper/1810.11363/) (2018)
- [BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representation](https://mlsanity.com/paper/2103.06695/) (2021)
- [Recurrent Convolutional Neural Networks help to predict location of Earthquakes](https://mlsanity.com/paper/2004.09140/) (2020)
- [Real-time data-driven detection of the rock type alteration during a directional drilling](https://mlsanity.com/paper/1903.11436/) (2019)
- [Application of Machine Learning to accidents detection at directional drilling](https://mlsanity.com/paper/1906.02667/) (2019)
- [Unsupervised anomaly detection for discrete sequence healthcare data](https://mlsanity.com/paper/2007.10098/) (2020)

## Built on by (most cited first, 6 of 6)

- [Non-contrastive representation learning for intervals from well logs](https://mlsanity.com/paper/2209.14750/) (2022)
- [Learning Transactions Representations for Information Management in Banks: Mastering Local, Global, and External Knowledge](https://mlsanity.com/paper/2404.02047/) (2024)
- [Self-supervised similarity models based on well-logging data](https://mlsanity.com/paper/2209.12444/) (2022)
- [Normalizing self-supervised learning for provably reliable Change Point Detection](https://mlsanity.com/paper/2410.13637/) (2024)
- [Robust representations of oil wells' intervals via sparse attention mechanism](https://mlsanity.com/paper/2212.14246/) (2022)
- [A theoretical framework for self-supervised contrastive learning for continuous dependent data](https://mlsanity.com/paper/2506.09785/) (2025)

## Abstract (arXiv)

One of the first steps during the investigation of geological objects is the interwell correlation. It provides information on the structure of the objects under study, as it comprises the framework for constructing geological models and assessing hydrocarbon reserves. Today, the detailed interwell correlation relies on manual analysis of well-logging data. Thus, it is time-consuming and of a subjective nature. The essence of the interwell correlation constitutes an assessment of the similarities between geological profiles. There were many attempts to automate the process of interwell correlation by means of rule-based approaches, classic machine learning approaches, and deep learning approaches in the past. However, most approaches are of limited usage and inherent subjectivity of experts. We propose a novel framework to solve the geological profile similarity estimation based on a deep learning model. Our similarity model takes well-logging data as input and provides the similarity of wells as output. The developed framework enables (1) extracting patterns and essential characteristics of geological profiles within the wells and (2) model training following the unsupervised paradigm without the need for manual analysis and interpretation of well-logging data. For model testing, we used two open datasets originating in New Zealand and Norway. Our data-based similarity models provide high performance: the accuracy of our model is 0.926 compared to 0.787 for baselines based on the popular gradient boosting approach. With them, an oil&gas practitioner can improve interwell correlation quality and reduce operation time.