Application of Data Mining in Well Control Management for Well Repair Operations

Miao Miao ()
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Miao Miao: Daqing Oilfield Co., Ltd., First Oil Production Plant

A chapter in Proceedings of the 2026 2nd International Conference on Data Mining and Project Management (DMPM 2026), 2026, pp 224-231 from Springer

Abstract: Abstract Well control in well repair operations is essential to safe and stable oilfield production, because failures in pressure control may lead to blowouts, environmental pollution, equipment damage, and even casualties. Traditional well control management mainly depends on manual inspection, experience-based judgment, and fixed emergency procedures, which makes it difficult to identify early abnormal signals in a timely and accurate manner. To improve the effectiveness of well control management, this paper discusses the application of data mining techniques in well repair operations. Time-series analysis and anomaly detection can be used to monitor the operating status of key well control equipment, while data-driven early warning models can support the identification of kick and blowout risks. By integrating equipment-state data, pressure-related signals, and operational information, data mining provides a more objective and timely basis for risk assessment and emergency response. This study shows that the introduction of data mining can strengthen process supervision, improve abnormal situation warning capability, and support the intelligent development of well control management in well repair operations.

Keywords: well control; data mining; well repair operations; anomaly detection; risk early warning (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6239-689-0_22

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DOI: 10.2991/978-94-6239-689-0_22

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