Approach for Real-Time Prediction of Pipe Stuck Risk Using a Long Short-Term Memory Autoencoder Architecture

Abstract Pipe-sticking during drilling operations causes severe difficulties, including economic losses and safety issues. Therefore, real-time 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 developed by combining an unsupervised learning model built using an encoder-decoder, long short-term memory architecture, with a relative error function. 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. An evaluation method of stuck-pipe possibilities using a relative error function reduced false predictors caused by large variations of drilling parameters. The prediction technique was then applied to 34 actual stuck-pipe events, where it was found that reconstruction errors calculated with the relative error function increased 0.5-10 hours prior to the pipe sticking for 17 out of 34 stuck-pipe events (thereby partly confirming our hypothesis).