  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.
