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• W4366778442 abstract "Abstract Detection of early signs of stuck pipe incidents is crucial because of the enormous costs of recovering from the incidents. Previous studies have leaned significantly toward a physics-based or data-science approach. However, both approaches have challenges, such as the uncertainty of the physics-based model and the lack of data in the data-science approach. This study proposes a hybrid approach using physical insights and data science and discusses the possibility of early detection of stuck pipes. The proposed method comprises two steps. In the first step, a data-driven model with physical insights is trained using the historical data of the in situ well to estimate some of the drilling variables. In the second step, the risk of stuck pipe occurrence (hereafter referred to as stuck risk) is calculated based on the historical and current measured data and the estimation of the trained model. This approach is expected to overcome the limitations of the previous methods as it allows the construction of a detection model tuned to the in situ well. In the case studies, models for estimating the top drive torque and standpipe pressure were constructed. The performance of the models is discussed using actual drilling data from drilling fields, including 21 stuck incidents during drilling operations. The proposed method was first examined using short-term output. The output confirmed that the stuck risk increased shortly before the stuck incident occurred in 15 cases. This increase in stuck risk was consistent with physical considerations. Subsequently, this study examined the long-term output over several months; this was rarely done in previous studies. Few false positives were observed in several cases even within this long-term output. Additionally, several model improvements were found to have the potential to further improve its performance. The novelty of our research lies in creating a broad framework for the early sign detection of stuck pipes by using both physical insights and data science methods. The proposed hybrid approach demonstrated the potential to reduce false alarms and improve interpretability compared to previous methods. The framework is highly extensible, and further performance improvements can be expected in the future." @default.
• W4366778442 created "2023-04-25" @default.
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• W4366778442 date "2023-04-24" @default.
• W4366778442 modified "2023-10-11" @default.
• W4366778442 title "Hybrid Approach Using Physical Insights and Data Science for Early Stuck Detection" @default.
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• W4366778442 doi "https://doi.org/10.4043/32532-ms" @default.
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