With the increase of drilling depth, complicated geological environments lead to a high risk of loss and kick. Fault diagnosis plays an essential role in minimizing the financial and environmental losses of the drilling process. On account of the temporal correlation of drilling parameters, a fault diagnosis method based on feature clustering of time series data for loss and kick of the drilling process is presented in this paper. Distance correlation is conducted for parameter combination to retain the whole information of drilling process. Global trend, local trends, and approximate entropy features are extracted to illustrate the characteristic of the time series. Density-based clustering method is performed for each combination to mine the local similarity among drilling parameters. Based on the clustering results of each combination as the inputs, the Bayesian classifier is further utilized to obtain the final fault diagnosis result. Experiments are executed with the actual data collected from a practical drilling process. The results indicate that the proposed method has both low false alarm rate and low miss alarm rate.

随着钻探深度的增加，复杂的地质环境导致高损失和高踢的风险。故障诊断在最大程度减少钻井过程的财务和环境损失方面起着至关重要的作用。鉴于钻井参数的时间相关性，提出了一种基于时间序列数据特征聚类的钻井过程损失和反冲的故障诊断方法。进行距离相关以进行参数组合，以保留钻井过程的全部信息。提取全局趋势，局部趋势和近似熵特征以说明时间序列的特征。对每个组合执行基于密度的聚类方法，以挖掘钻井参数之间的局部相似性。基于每个组合的聚类结果作为输入，进一步利用贝叶斯分类器获得最终的故障诊断结果。使用从实际钻孔过程中收集的实际数据执行实验。结果表明，该方法具有较低的误报率和较低的漏报率。