A real-time stuck pipe prediction methodology is proposed in this paper. We assume early signs of stuck pipe to be apparent when the drilling data behavior deviates from that from normal drilling operations. The definition of normalcy changes with drill string configuration or geological conditions. Here, a depth-domain data representation is adopted to capture the localized normal behavior. Several models, based on auto-encoder and variational auto-encoders, are trained on regular drilling data extracted from actual drilling data. When the trained model is applied to data sets before stuck incidents, eight incidents showed large reconstruction errors. These results suggest better performance than the previously reported supervised approach. Inter-comparison of various models reveals the robustness of our approach. The model performance depends on the featured parameter suggesting the need for multiple models in actual operation.

翻译：本文提出了一种实时卡钻预测方法。我们假设当钻井数据行为偏离正常钻井作业模式时，卡钻早期迹象即可显现。钻柱配置或地质条件的变化会导致正常状态定义发生改变。本文采用深度域数据表征方法捕捉局部正常行为。基于自编码器和变分自编码器的多种模型，利用从实际钻井数据中提取的常规钻井数据进行训练。将训练后的模型应用于卡钻事故发生前的数据集时，有8起事故显示出显著的重构误差。结果表明该方法优于此前报道的监督式方法。不同模型的交叉比较揭示了本方法的稳健性。模型性能取决于特征参数选择，表明实际作业中需部署多种模型。