机器学习在井漏预测中的研究进展

Research Progress on Machine Learning in Lost Circulation Prediction

摘要: 井漏是钻井工程中最常见、危害最严重的事故之一，传统基于经验与单一参数阈值的预警方法存在准确率低、响应滞后等局限。近年来，随着钻井数据采集能力的发展，机器学习技术因其强大的非线性映射与多参数融合能力，为井漏智能预测提供了全新的技术路线。文章系统综述了机器学习在井漏预测中的研究进展，重点介绍了随机森林、支持向量机、梯度提升树等传统机器学习模型，以及BP神经网络、长短期记忆网络等深度学习模型的应用现状及技术特征。通过梳理国内外相关研究，总结了当前研究的主要发展进程与成果，并对未来多源数据融合、实时智能预警平台等发展趋势进行了展望与分析。

Abstract: Well bore leakage remains one of the most common and hazardous incidents in drilling operations. Traditional early warning methods relying on empirical data and single-parameter thresholds exhibit limitations such as low accuracy and delayed response. With advancements in drilling data acquisition capabilities, machine learning technologies—particularly their robust nonlinear mapping and multi-parameter fusion capabilities—have provided innovative technical approaches for intelligent well bore leakage prediction. This paper systematically reviews research progress in machine learning applications for leakage prediction, focusing on traditional models including random forests, support vector machines, and gradient boosting trees, as well as deep learning models such as BP neural networks and long short-term memory networks. By reviewing relevant studies at home and abroad, the main development processes and achievements of current research are summarized, and future development trends such as multi-source data fusion and real-time intelligent early warning platforms are prospected and analyzed.