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[2511.00129] Data-Augmented Deep Learning for Downhole Depth Sensing and Validation
Summary
This paper presents a novel approach to downhole depth sensing using data-augmented deep learning techniques, addressing challenges in dataset limitations and preprocessing methods for casing collar recognition.
Why It Matters
Accurate depth measurement is crucial in oil and gas operations for efficiency and safety. This research enhances neural network training methodologies, particularly in data-scarce environments, paving the way for improved automation in downhole operations.
Key Takeaways
- Introduces a system for CCL log acquisition to enhance dataset construction.
- Proposes effective data augmentation methods that improve model generalization.
- Demonstrates significant F1 score improvements in baseline models through augmentation techniques.
- Addresses existing gaps in data augmentation for casing collar recognition.
- Provides a foundation for future automation in downhole operations.
Computer Science > Machine Learning arXiv:2511.00129 (cs) [Submitted on 31 Oct 2025 (v1), last revised 25 Feb 2026 (this version, v4)] Title:Data-Augmented Deep Learning for Downhole Depth Sensing and Validation Authors:Si-Yu Xiao, Xin-Di Zhao, Tian-Hao Mao, Yi-Wei Wang, Yu-Qiao Chen, Hong-Yun Zhang, Jian Wang, Jun-Jie Wang, Shuang Liu, Tu-Pei Chen, Yang Liu View a PDF of the paper titled Data-Augmented Deep Learning for Downhole Depth Sensing and Validation, by Si-Yu Xiao and 10 other authors View PDF HTML (experimental) Abstract:Accurate downhole depth measurement is essential for oil and gas well operations, directly influencing reservoir contact, production efficiency, and operational safety. Collar correlation using a casing collar locator (CCL) is fundamental for precise depth calibration. While neural network has achieved significant progress in collar recognition, preprocessing methods for such applications remain underdeveloped. Moreover, the limited availability of real well data poses substantial challenges for training neural network models that require extensive datasets. This paper presents a system integrated into a downhole toolstring for CCL log acquisition to facilitate dataset construction. Comprehensive preprocessing methods for data augmentation are proposed, and their effectiveness is evaluated using baseline neural network models. Through systematic experimentation across diverse configurations, the contribution of each augmentation method is analyze...
