基于MWSPCA-CBR的智能预警方法研究及其在石化工业中的应用

石油钻井是一项高风险性、耗资巨大的系统工程。为了智能预警石油钻井过程中的异常,缩短非生产时间,降低相关风险,提出一种基于移动窗稀疏主元分析法（MWSPCA）的案例推理（CBR）异常智能预警方法（MWSPCA-CBR）。首先利用MWSPCA算法分析钻井过程中的实时数据,快速定位出异常可能发生的时间,然后使用基于案例推理方法分析异常数据,确定可能的异常类型,并为实时监控专家提供相关异常的处理方法。所提方法应用到石油钻井过程异常预警中,实验结果验证了所提方法的可行性和有效性,为钻井过程降低风险成本提供了新思路。

Improved intelligent warning method based on MWSPCA-CBR and its application in petrochemical industries

The petroleum drilling project is a high-risk and costly system project. To effectively scan for potential problems of drilling, reduce non-productive time and lower related risks, this paper proposes an improved intelligent warning method based on moving window sparse principal component analysis (MWSPCA) integrating case-based reasoning (CBR) (MWSPCA-CBR). First, the MWSPCA is used to analyze the real-time data in the drilling process, and the time of occurrence of the anomaly is quickly located. Then the abnormal data is analyzed by using the CBR method to give possible exception types, and the associated handling methods are provided for monitoring experts. Finally, the proposed method is applied to intelligent warn abnormal problems of the petroleum drilling, the experimental results verify the feasibility and effectiveness of the proposed method and provide new ideas for reducing risks and costs during the petroleum drilling process.