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 analyzed. Results demonstrate that standardization, label distribution smoothing, and random cropping are fundamental prerequisites for model training, while label smoothing regularization, time scaling, and multiple sampling significantly enhance model generalization capabilities. Incorporating the proposed augmentation methods into the two baseline models results in maximum F1 score improvements of 0.027 and 0.024 for the TAN and MAN models, respectively. Furthermore, applying these techniques yields F1 score gains of up to 0.045 for the TAN model and 0.057 for the MAN model compared to prior studies. Performance evaluation on real CCL waveforms confirms the effectiveness and practical applicability of our approach. This work addresses the existing gaps in data augmentation methodologies for training casing collar recognition models under CCL data-limited conditions, and provides a technical foundation for the future automation of downhole operations.

翻译：精确的井下深度测量对于油气井作业至关重要，直接影响储层接触、生产效率和作业安全。使用套管接箍定位器（CCL）的接箍对比是进行精确深度标定的基础。尽管神经网络在接箍识别方面取得了显著进展，但此类应用的预处理方法仍不成熟。此外，真实井数据的有限可用性对需要大量数据集的神经网络模型训练构成了重大挑战。本文提出了一种集成到井下工具串中的系统，用于采集CCL测井数据以促进数据集构建。提出了用于数据增强的综合性预处理方法，并使用基线神经网络模型评估了其有效性。通过对不同配置进行系统实验，分析了每种增强方法的贡献。结果表明，标准化、标签分布平滑和随机裁剪是模型训练的基本前提，而标签平滑正则化、时间缩放和多重采样则能显著提升模型的泛化能力。将所提出的增强方法纳入两个基线模型后，TAN和MAN模型的最大F1分数分别提升了0.027和0.024。此外，与先前研究相比，应用这些技术使TAN模型的F1分数最高提升0.045，MAN模型最高提升0.057。在真实CCL波形上的性能评估证实了我们方法的有效性和实际适用性。这项工作弥补了在CCL数据有限条件下训练套管接箍识别模型时数据增强方法存在的不足，并为未来井下作业自动化提供了技术基础。