Kick is considered as an early warning sign to the blowout that is among the most undesired and feared accidents during drilling operations. Kick detection system is commonly used to timely identify the occurrence of a kick. The method commonly used for kick detection relies on the proper selection of monitoring indicators. A kick detection system should not only have very high accuracy but also maintain reliable over a long time. Different from the existing studies focusing on improving the detection accuracy, this paper presents a frame emphasizing on quantitatively analyzing and enhancing the reliability of the kick detection sensor networks. The dynamic Bayesian network (DBN) for the sensor networks is established that employs Markov chain to obtain the reliability degradation of measurement sensors over time. The proposed method is applied and evaluated by case studies to conduct reliability and sensitivity analysis for kick detection sensor networks. The reliability analysis results demonstrate that the proposed method can quantitatively analyze the reliability of a kick detection sensor networks consisting of various sensors over given time periods. The sensitivity analysis results indicate that the proposed method is effective in identifying the critical sensors that have the greatest effect on the reliability of one certain kick detection system. Based on the analysis results, optimized logical combination of sensors of a kick detection system can be achieved. An improved sensor network for the unreliable case was proposed and evaluated.

踢被认为是井喷的预警信号，是钻井作业期间最不希望发生的事故。踢球检测系统通常用于及时识别踢球的发生。通常用于脚踢检测的方法依赖于监视指标的正确选择。脚踢检测系统不仅应具有很高的准确性，而且应长期保持可靠。与现有的侧重于提高检测精度的研究不同，本文提出了一种框架，侧重于定量分析和增强反冲检测传感器网络的可靠性。建立了用于传感器网络的动态贝叶斯网络（DBN），该网络使用马尔可夫链来获得随时间变化的测量传感器的可靠性下降。通过实例研究，对所提出的方法进行了应用和评估，以对脚踢检测传感器网络进行可靠性和灵敏度分析。可靠性分析结果表明，该方法可以定量分析在给定时间段内由各种传感器组成的反冲检测传感器网络的可靠性。灵敏度分析结果表明，所提出的方法可以有效地识别对一个脚踢检测系统的可靠性影响最大的关键传感器。基于分析结果，可以实现脚踢检测系统的传感器的优化逻辑组合。提出并评估了一种针对不可靠情况的改进传感器网络。可靠性分析结果表明，该方法可以定量分析在给定时间段内由各种传感器组成的反冲检测传感器网络的可靠性。灵敏度分析结果表明，所提出的方法可以有效地识别对一个脚踢检测系统的可靠性影响最大的关键传感器。基于分析结果，可以实现脚踢检测系统的传感器的优化逻辑组合。提出并评估了一种针对不可靠情况的改进传感器网络。可靠性分析结果表明，该方法可以定量分析在给定时间段内由各种传感器组成的反冲检测传感器网络的可靠性。灵敏度分析结果表明，所提出的方法可以有效地识别对一个脚踢检测系统的可靠性影响最大的关键传感器。基于分析结果，可以实现脚踢检测系统的传感器的优化逻辑组合。提出并评估了一种针对不可靠情况的改进传感器网络。灵敏度分析结果表明，所提出的方法可以有效地识别对一个脚踢检测系统的可靠性影响最大的关键传感器。基于分析结果，可以实现脚踢检测系统的传感器的优化逻辑组合。提出并评估了一种针对不可靠情况的改进传感器网络。灵敏度分析结果表明，所提出的方法可以有效地识别对一个脚踢检测系统的可靠性影响最大的关键传感器。基于分析结果，可以实现脚踢检测系统的传感器的优化逻辑组合。提出并评估了一种针对不可靠情况的改进传感器网络。