An Unsupervised Learning Model for Pipe Stuck Predictions Using a Long Short-Term Memory Autoencoder Architecture

Abstract Pipe-sticking during drilling operations causes severe difficulties, including economic losses and safety issues. Therefore, stuck-pipe predictions are an important tool to preempt this problem and avoid the aforementioned troubles. In this study, we have developed a prediction technique based on artificial intelligence, in collaboration with industry, the government, and academia. This technique was an unsupervised learning model built using an encoder-decoder, long short-term memory architecture. The model was trained with the time series data of normal drilling operations and based on an important hypothesis: reconstruction errors between observed and predicted values are higher around the time of pipe sticking than during normal drilling operations. The trained model was then applied to 34 actual stuck-pipe events, where it was found that reconstruction errors increased prior to the pipe sticking in some cases (thereby partly confirming our hypothesis) and were sensitive to large variations in the drilling parameters.