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的行为偏离正常钻井作业的数据行为时，卡钻的早期迹象就会显现。正常状态的定义会随着钻柱配置或地质条件的变化而改变。本文采用深度域数据表示方法来捕获局部正常行为。基于自编码器和变分自编码器的多个模型在实际钻井数据中提取的常规钻井数据上进行了训练。当将训练好的模型应用于卡钻事故发生前的数据集时，有8起事故显示出较大的重构误差。这些结果表明，该方法优于先前报道的监督方法。各模型间的相互比较揭示了本方法的鲁棒性。模型性能依赖于特征参数，这表明在实际操作中需要采用多个模型。