# Federated Learning for Cross-block Oil-water Layer Identification

arXiv: https://arxiv.org/abs/2112.14359 · HTML: https://mlsanity.com/paper/2112.14359/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 32 references that are in the map.
- First posted on arXiv 29 Dec 2021.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Decoupling Representation and Classifier for Long-Tailed Recognition](https://mlsanity.com/paper/1910.09217/) (2019)
- [Deep Long-Tailed Learning: A Survey](https://mlsanity.com/paper/2110.04596/) (2021)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Equalization Loss for Long-Tailed Object Recognition](https://mlsanity.com/paper/2003.05176/) (2020)
- [Distribution Alignment: A Unified Framework for Long-tail Visual Recognition](https://mlsanity.com/paper/2103.16370/) (2021)
- [Dynamic Fusion based Federated Learning for COVID-19 Detection](https://mlsanity.com/paper/2009.10401/) (2020)
- [Mask Attention Networks: Rethinking and Strengthen Transformer](https://mlsanity.com/paper/2103.13597/) (2021)
- [Federated Learning for COVID-19 Detection with Generative Adversarial Networks in Edge Cloud Computing](https://mlsanity.com/paper/2110.07136/) (2021)
- [GFL: A Decentralized Federated Learning Framework Based On Blockchain](https://mlsanity.com/paper/2010.10996/) (2020)

## Abstract (arXiv)

Cross-block oil-water layer(OWL) identification is essential for petroleum development. Traditional methods are greatly affected by subjective factors due to depending mainly on the human experience. AI-based methods have promoted the development of OWL identification. However, because of the significant geological differences across blocks and the severe long-tailed distribution(class imbalanced), the identification effects of existing artificial intelligence(AI) models are limited. In this paper, we address this limitation by proposing a dynamic fusion-based federated learning(FL) for OWL identification. To overcome geological differences, we propose a dynamic weighted strategy to fuse models and train a general OWL identification model. In addition, an F1 score-based re-weighting scheme is designed and a novel loss function is derived theoretically to solve the data long-tailed problem. Further, a geological knowledge-based mask-attention mechanism is proposed to enhance model feature extraction. To our best knowledge, this is the first work to identify OWL using FL. We evaluate the proposed approach with an actual well logging dataset from the oil field and a public 3W dataset. Experimental results demonstrate that our approach significantly out-performs other AI methods.