Wellbore geometry stuck pipe mechanism occurs when the string and the well are incompatible with each other. This issue is commonly related to changes in hole diameter, angle, and direction associated with symptoms such as mobile/swelling formation, undergauged hole, key seating, ledges, and high doglegs. An internal study identified that many stuck pipe incidents were associated with mechanical sticking, specifically wellbore geometry sticking with high-cost impact, which warrants proactive prevention. Throughout this paper, we provide and demonstrate how machine learning solutions can foresee the potential stuck pipe related to wellbore geometry issues based on two signs: hookload signature and dogleg severity. The application is based on the Artificial Neural Network (ANN) approach that reads the surface parameters sequence of hookload real-time data and learns with historical wells data. Machine learning (ML) then determines how the hookload behaves for each type of activity (tripping and drilling). The machine learning predictions can then be streamed on a web-based application accessible to the operations and project team. The neural network design for hookload prediction while tripping in/out considers a drag when the string moves towards a region with doglegs severity higher than the threshold chosen based on engineering judgment. This paper also discusses applications beyond real-time estimation, such as predicting the trend of the few subsequent expected hook loads up to 6 to 10 stands ahead based on case studies from previous live wells obtained from the real-time monitoring center where the product is used. The output from the machine learning solution provided a basis for risk identification and further analysis by the monitoring specialist in a proactive intervention effort to prevent stuck pipe incidents. The implementation of applications described in this paper could detect an early symptom of wellbore geometry issue; hence proactive action can be taken to avoid a potential stuck pipe event.

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