机器学习在井漏监测中的研究进展

Research progress of machine learning in well loss monitoring

井漏是钻井过程中常发生的钻井事故,具有很强的突发性,难以及时发现。在大数据和人工智能技术下,数字化和智能化防漏技术已成为不可避免的发展趋势。这些技术的核心内容包括基于机器学习的算法模型和相应的系统软件。文章通过对井漏机理的梳理归纳,阐述了井漏发生的特点,同时进一步归纳了BP神经网络、支持向量机、随机森林等机器学习算法在井漏预测预警中的应用以及智能化井漏监测系统研究现状。与传统的人工判断井漏事故发生相比,通过机器学习算法能够更加提前、更加可靠、更加精准的对井漏发生进行预测预警。同时通过智能化井漏监测系统,工程师可以更加直观的了解井下或者井上各种参数的变化,通过这些变化和系统的推荐和预警,更快的作出反应,极大的提高钻井安全。

Loss is a common drilling accident in the drilling process,which is very sudden and difficult to find in time.Under the big data and artificial intelligence technology,digital and intelligent leak prevention technology has become an inevitable trend of development.The core content of these technologies includes algorithm model based on machine learning and corresponding system software.By combing and summarizing the mechanism of leakage,the paper expounds the characteristics of leakage occurrence,and further summarizes the application of BP neural network,support vector machine,random forest and other machine learning algorithms in the prediction and early warning of leakage,as well as the research status of intelligent leakage monitoring system.Compared with the traditional manual judgment of the occurrence of the well loss,the machine learning algorithm can predict the occurrence of the well loss more in advance,more reliably and more accurately.In addition,through the intelligent loss monitoring system,engineers can more directly understand downhole or downhole changes in various parameters,through these changes and system recommendations and early warning,faster response,greatly improving drilling safety.