石油钻采工艺2016，Vol.38Issue(3)：291-295,5.DOI:10.13639/j.odpt.2016.03.003
基于马尔科夫链和贝叶斯网络的钻井风险预测
Drilling risk prediction based on Markov chain and Bayesian network
摘要
Abstract
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 drill-ing 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 ift between the vertical prediction and the actual data of the integrated method is higher than that of Markova chain (82%) and Bayesian network (46%).关键词
钻井风险/风险预测/马尔科夫链/贝叶斯网络Key words
drilling risk/risk prediction/Markov chain/Bayesian network分类
能源科技引用本文复制引用
钟仪华,刘雨鑫,林旭旭..基于马尔科夫链和贝叶斯网络的钻井风险预测[J].石油钻采工艺,2016,38(3):291-295,5.基金项目
西南石油大学创新团队基金项目“最优化理论与控制”（编号2013XJZT004）。 （）
