The early detection of a stuck pipe event is crucial as it is one of the major incidents resulting in nonproductive time. An ordinary supervised machine learning approach has been adopted to achieve the detection of stuck pipe in some previous studies. However, for early detection before stuck occurs with this approach, there are challenging issues such as limited stuck pipe data, various causes of stuck, and the lack of a prior exact "stuck sign" which should be a label in the training dataset. In this study, the surface drilling data is first collected from multiple agencies to enhance the training dataset. Subsequently, a supervised machine learning algorithm with ordinary binary classification methodologies, such as support vector machines and neural networks is adopted. The supervised machine learning approach presents good performance for stuck pipe event detection. However, it detects "stuck has already occurred", and it cannot effectively predict the stuck pipe because there is no exact sign for stuck pipe which is mandatory as label for training data. This study also adopts an unsupervised machine learning algorithm which employs architectures that include an autoencoder with long short-term memory, as well as a multiple prediction model to improve the expressiveness. The unsupervised machine learning process typically involves learning the features of normal activities, whereby the created model can represent only these activities. When stuck occurs or will occur, as such data are not represented by the created model, it should be detected. The performance of the early stuck pipe event detection using supervised and unsupervised machine learning approaches is analyzed, and the results demonstrate that the unsupervised machine learning approach presents a better early stuck pipe detection capability. The proposed machine learning algorithm will be further improved in the future and the prediction result will be validated through actual operation.
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