Drilling risk prediction based on Markov chain and Bayesian network

Drilling operation is a risky and costly process, during which many uncertainties may cause a serious accident. In order to prevent or mitigate the risks and thereby avoid economic loss, it is necessary to predict these uncertainties. In this paper, the existing drilling risk prediction methods (e.g. Markova chain and Bayesian network) were reviewed, and then a new drilling risk prediction method was proposed by integrating the Markova chain and Bayesian network based on the index system adopted on site. This new method can be used predict the risk of drilling accident vertically and horizontally, and also overcome the shortage which occurs when the upper indices are processed only by using Markova chain. Moreover, it provides the theoretical basis for the risk diagnosing, monitoring and controlling. The case study shows that this new method is correct and feasible. The goodness of fit between the vertical prediction and the actual data of the integrated method is higher than that of Markova chain (82%) and Bayesian network (46%).