An End-To-End Machine Learning Project for Detection of Stuck Pipe Symptoms During Tripping Operations

Non-productive time due to stuck pipe costs the Oil and Gas industry substantial losses amounting to $250 million annually [1]. Thus, it is imperative for companies to invest in tools that can aid in prevention. This study integrates different concepts and methodologies from Petroleum Engineering, Data Analysis, and Machine Learning (ML). It aims to identify and extract hook load signatures before a stuck pipe event that can be used to train an ML model. The lack of transparent and consistent frameworks in many published papers using the same approach proved to be a problem. Hence, it is also our aim to present all the algorithms used. In a Machine Learning project, data preparation accounts for about 80% of the work [2, 3]. For this reason, the author developed two web-based applications for cleaning and exploring raw drilling data. These provided time savings given the time constraints of this project. Once the data was prepared, maximum and local minimum hook loads were extracted for tripping out and tripping in operations, respectively. During the study, a new concept for extracting the local minimum hook load was developed. It was able to identify the trend deviation as early as 4 hours and 30 minutes before the reported stuck pipe. Furthermore, all the extracted maximum and local minimum hook loads distinguished trend deviation between normal and deteriorating downhole conditions. This was not possible when basing solely on the real-time hook load. Moreover, a long short term-memory network was trained using 50% of the extracted hook load signatures. This model was designed to predict and identify hook load trends during tripping operations. Then using the remaining data, the model was evaluated. Results showed that the model predicted hook loads with a mean absolute error of