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Title: PREVENTING PIPE STICKING IN OIL AND GAS WELLS USING AN IMPROVED TORQUE AND DRAG MODEL, ALONG WITH ANALYTICAL AND MACHINE LEARNING METHODS
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PREVENTING PIPE STICKING IN OIL AND GAS WELLS USING AN IMPROVED TORQUE AND DRAG MODEL, ALONG WITH ANALYTICAL AND MACHINE LEARNING METHODS
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10.5593/sgem2024/1.1
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1314-2704
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English
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24
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1.1
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• Prof. DSc. Oleksandr Trofymchuk, UKRAINE
• Prof. Dr. hab. oec. Baiba Rivza, LATVIA |
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Effectively preventing pipe sticking in oil and gas wells is a key aspect of safety and productivity in the oil and gas industry. This paper presents a new torque and drag model developed using machine learning techniques to improve accuracy and predictive capabilities. The model is based on a comprehensive analysis of many factors, including geological characteristics of the well, drilling parameters, fluid parameters, hydraulic conditions, and production equipment parameters. This research explores the application of a chained regression model combining Multilayer Perceptron (MLP) and XGBoost for predicting multiple physical properties in the oil and gas industry. The study aims to enhance prediction accuracy in hydraulics, torque, and drag by leveraging the strengths of both models. The model will help improve the monitoring and interpretation of drilling data streams. Besides the model will help detect signs of other interfering problems in the downhole wellbore conditions and take the necessary measures in advance to prevent them. As a result, it will help optimize costs, minimize non-productive time, and improve safety.
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conference
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Proceedings of 24th International Multidisciplinary Scientific GeoConference SGEM 2024
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24th International Multidisciplinary Scientific GeoConference SGEM 2024, 1 - 7 July, 2024
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Proceedings Paper
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STEF92 Technology
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International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM
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SWS Scholarly Society; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Russian Acad Sci; Serbian Acad Sci and Arts; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; European Acad Sci, Arts and Letters; Acad Fine Arts Zagreb Croatia; Croatian Acad Sci and Arts; Acad Sci Moldova; Montenegrin Acad Sci and Arts; Georgian Acad Sci; Acad Fine Arts and Design Bratislava; Russian Acad Arts; Turkish Acad Sci.
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651-658
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1 - 7 July, 2024
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website
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9909
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torque and drag, machine learning, drilling, pipe sticking, regression model.
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