Detection of Stuck Pipe Early Signs and the Way Toward Automation

The earlier a stuck pipe incident is predicted and mitigated, the higher the chance of success in freeing the pipe or avoiding severe sticking in the first place. Time is crucial in such cases as an improper reaction to a stuck pipe incident can easily make it worse. In this work, a novel and practical model was developed using real-time drilling data to automatically detect leading signs of stuck pipe during drilling operations and communicate the observations and alerts, sufficiently ahead of time, to the rig crew for avoidance or remediation actions to be taken. The model uses key drilling parameters to detect abnormal trends that are identified as leading signs to stuck pipe. The parameters and patterns used in building the system were identified from published literature and historical data and reports of stuck pipe incidents. The model is designed to be implemented in the real-time drilling data portal to provide an alarm system for all oil and gas rigs based on the observed abnormalities. The alarm is to be populated on the real-time environment and communicated to the rig crew in a timely manner to ensure optimal results, giving them more time to prevent or remediate a potential stuck pipe incident. Testing the model on several wells showed promising results as anomalities were detected early in time before the actual stuck pipe incidents were reported. It further facilitated better understanding of the underlying physics principles and provided awareness of stuck pipe occurance. It improved monitoring and interpretating the drilling data streams. Beside such pipe signs, the model helped detecting signs of other impeding problems in the downhole conditions of the wellbore, the drilling equipment, and the sensors. The model exceptionally uses the robustness of data-based along with the physics-based analysis of stuck pipe. This hybrid model has shown effective detection of the signs observed by experts ahead of time and has helped providing enhanced stuck pipe prediction and risk assessment.