Abstract Kick, or hydrocarbon influx, is one of the significant challenges during the drilling operation. A kick happens when the formation pressure exceeds the hydrostatic pressure of mud weight. Detection of a kick at an early stage spares more time to take necessary actions to prevent its growth and mitigate the potential well blowout. There are varieties of methods applied for early kick detection. The conventional method entails monitoring surface parameters which leads to delay in the detection. Some recent works show the ability to employ monitoring of downhole parameters to realize early kick detection. Data-driven Bayesian Network (BN) has shown to solve problems in complex systems where the knowledge about the system is not adequate to apply a model-based method. Data-driven BN creates a model based on historical data, which is usually available, unlike expensive, and often insufficient, expert knowledge. Using the data obtained in a laboratory-scale experiment, this paper presents the application of data-driven BN model in using downhole parameters to early kick detection.

中文翻译：

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工业钻井过程中用于早期突波检测的数据驱动贝叶斯网络模型

摘要 井涌或烃类涌入是钻井作业过程中面临的重大挑战之一。当地层压力超过泥浆重量的静水压力时，就会发生井涌。在早期检测井涌可以腾出更多时间采取必要措施来防止井涌增长并减轻潜在的井喷。有多种方法可用于早期踢腿检测。传统方法需要监测导致检测延迟的表面参数。最近的一些工作显示了采用井下参数监测来实现早期井涌检测的能力。数据驱动的贝叶斯网络 (BN) 已证明可以解决复杂系统中的问题，在这些系统中，有关系统的知识不足以应用基于模型的方法。数据驱动的BN基于历史数据创建模型，与昂贵且通常不足的专业知识不同，这通常是可用的。使用在实验室规模实验中获得的数据，本文介绍了数据驱动的 BN 模型在使用井下参数进行早期井涌检测中的应用。