深度学习在井漏监测中的研究进展

Research Progress of Deep Learning in Lost Circulation Monitoring

摘要: 井漏是钻井作业中最常见的钻井事故之一，受工况转换、传感器噪声以及井下复杂情况的影响，传统的井漏监测方法通常难以及时、准确地识别风险。随着大数据与人工智能技术的发展，基于深度学习的数字化、智能化井漏监测技术已成为当前发展的主流趋势，其核心技术主要包括基于深度学习算法模型的构建和智能化监测系统的应用。文章通过分析井漏的成因和分类，归纳了其发生的主要特点，同时进一步阐述了循环神经网络、长短时记忆网络、门控循环单元在井漏监测中的应用现状与研究进展。与传统人工经验或阈值法相比，通过应用深度学习算法可以更早、更稳定、更精准地从多维时序数据中识别异常变化，捕捉各参数之间的协同变化模式，准确识别复杂工况的转变，从而降低误报与漏报，提升预警的准确性。

Abstract: Lost circulation is characterized by abrupt occurrence, strong coupling with drilling operations, and weak anomaly signals. Single-parameter threshold methods are therefore prone to false and missed alarms under operating-mode transitions, sensor noise, and missing data. This paper reviews the application of recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and gated recurrent units (GRUs) to lost circulation monitoring. The three architectures are compared in terms of model complexity, short- and long-term dependency modeling, noise robustness, computational cost, and scenario suitability. Key challenges in drilling time-series data, including noise and drift, missing records, class imbalance, and feature engineering, are then discussed together with commonly used solutions, such as filtering, interpolation, cost-sensitive learning, SMOTE, and feature-importance analysis. An end-to-end implementation pathway is proposed, covering data acquisition, edge preprocessing, standardized transmission, model inference, risk fusion, alarm delivery, and feedback-based model updating. The review indicates that RNNs are suitable for short windows and low-compute applications, LSTMs provide stronger long-term memory at higher computational cost, and GRUs offer a practical balance between accuracy and efficiency. Current studies remain limited by small numbers of wells, inconsistent labels, insufficient well-wise validation, and limited closed-loop field evaluation. Future work should emphasize multi-source data fusion, independent cross-well testing, lightweight and interpretable models, and long-term field deployment.