Abstract: Focusing on the issue that Principal Component Analysis-Bayes Discriminant Analysis (PCA-BDA) only supports safety evaluation but can not detect the dangerous factors, by introducing the concept of attribute importance degree, an improved PCA-BDA algorithm was proposed and applied to the petroleum drilling safety evaluation. Firstly, the safety ranking of each record was evaluated by the initial PCA-BDA algorithm. Secondly, the attribute importance was computed with the eigenvector matrix in PCA, the classification function coefficient in BDA, and the weight of safety ranking. Finally, the attributes were regulated and controlled with referencing the attribute importance. In the comparison experiments with Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation (FCE), the accuracy rate of improved PCA-BDA reached 96.7%, which was obviously higher than that of the AHP and FCE method. In the simulation experiment, more than 70% of safety rankings of petroleum drilling were improved by regulating the 3 most important attributes, while the safety ranking had no change by adjusting the least 3 important attributes. The experimental results show that the improved PCA-BDA can accurately accomplish the safety evaluation, and find out the critical attributes to make the petroleum drilling safety management more targeted.

Key words: attribute importance degree, Bayes Discriminant Analysis (BDA), Principal Component Analysis (PCA), petroleum drilling safety evaluation

摘要： 针对主成分分析-贝叶斯判别法（PCA-BDA）仅支持安全评价但不能发现危险因素的问题，引入属性重要度的概念，提出一种改进的PCA-BDA算法，并将其应用于石油钻井安全评价。首先，使用原始PCA-BDA方法评估出各条记录的安全等级；然后，利用主成分分析（PCA）过程中的特征向量矩阵，贝叶斯判别（BDA）过程中的判别函数矩阵，以及各安全等级的权重计算得出属性重要度；最后，通过参考属性重要度来调控属性。安全评价准确率的对比实验中，改进PCA-BDA方法准确率达到96.7%，明显高于层次分析法（AHP）和模糊综合评价法（FCE）。调控属性的仿真实验中，调控重要度最高的3个属性70%以上的钻井安全等级得到改善；相对地，调控重要度最低的3个属性钻井安全等级几乎没有变化。实验结果表明，改进PCA-BDA方法不仅能够准确地实现安全评价，同时能够找出关键属性使石油钻井安全管理更有针对性。

关键词: 属性重要度, 贝叶斯判别分析, 主成分分析, 石油钻井安全评价