Lost circulation and kick accidents warning based on Bayesian network for the drilling process

Abstract:

In recent years, geological drilling has made great progress in the field of drilling equipment and technology, but research on drilling accidents warning is still insufficient. To ensure safety and efficiency of the drilling process and reduce the losses caused by accidents, a lost circulation and kick accidents warning method for the drilling process is proposed in this paper. First, the drilling parameters that characterize accidents are selected through the analysis of accidents. Second, considering the uncertainty of the changes of drilling parameters when an accident occurs, a lost circulation and kick accidents warning model is established based on Bayesian network. Third, to effectively extract the trend of drilling parameters from actual drilling data with noises, the normalization, moving average and least square linear fitting methods are jointly used to judge the node status. Finally, the actual drilling data is used to verify the lost circulation and kick accidents warning model. The influence of different trend judgment boundaries and moving windows on the alarm performance is discussed. The experimental results show that the warning model effectively provides warning of the lost circulation and kick accidents. And a proper trend judgment boundary and moving window can reduce alarm delay, false alarm and missed alarm.