基于贝叶斯网络的钻井作业现场风险评估

摘要

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 man-made 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.%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。