基于贝叶斯网络的钻井作业现场风险评估

针对高投入、高风险和不确定性的钻井作业现场，展开了安全评价研究。提出了一种基于贝叶斯网络定量评
价钻井作业现场风险、寻找风险源的方法。通过分析历史数据与借助专家经验识别不安全因素，将影响钻井作业现场
安全性的32 个因素分为“人的不安全行为”和“物的不安全状态”，同时构建了钻井作业现场安全性的贝叶斯网络拓
扑结构，并进行了概率推理向前预测和向后诊断，定量评估了钻井作业现场安全性，找出了影响最突出的不安全因素。
将其应用于龙岗气田L 井钻井作业现场，得出人为的不安全行为和物的不安全状态概率分别为0.108 和0.165，整个L
井作业现场不安全概率为0.137，并诊断出过程监控缺陷、安全防护设施缺失、作业导致隐患、井控设备缺陷、生产管
理缺陷不安全因素最突出，与现场实际情况一致。该评价方法为现场安全作业提供较为准确的诊断依据。

Drilling Site Risk Assessment Based on Bayesian Network

In view of the high investment and risk and uncertainties in drilling operation，the safety evaluation about the
drilling operation is carried out in the paper. The method of evaluating risk and seeking risk resource during drilling operation
has been developed by using Bayes network. The 32 risk factors during the drilling operation could be classified into manmade
risk factors and natural risk factors by analyzing the history data and identifying the dangerous factors with the help of
expertise. The Bayes network topological structure and conditional probability table（CPT）was developed for drilling operation
risk；the probability was predicted forward and diagnosed backward；the safety probability of drilling operation was evaluated
quantitative and the most dangerous factor was found out. After applying the Bayes network model to Well L gas drilling
operation，we got the risk probability of man-made risk and natural risk at 0.108 and 0.165，respectively，the risk probability
of Well L gas drilling operation at 0.137. The many dangerous factors are defects in monitor during the drilling process，lack of
security protection facilities，hidden trouble induced by drilling operation，defect in well-control equipment and management
in production. This will provide precise diagnostic data for operators and decision-making for safe production.