Abstract Automatic and early drilling risks detection is a significant issue of drilling cost reduction and drilling efficiency improvement. In this paper, considering the inherent nature of drilling process data, a novel drilling risks monitoring method which can automatically detect drilling risks in real time was developed. The newly proposed method integrated wellbore hydraulics model and streaming-data-driven model parameter inversion algorithm to realize drilling risks detection. Through the in-depth analysis of several drilling risks’ common response characteristics, two drilling risk indicators, i.e. the pressure-loss factor and the flow-rate factor, were defined firstly. Then the drilling risks monitoring model was established to model the pressure and the flow rate responses. In the model, the pressure-loss factor and the flow-rate factor were model parameters which need to be inversed using streaming-data. Finally, streaming-data-driven model parameter inversion algorithm was adopted to estimate the pressure-loss factor and the flow-rate factor in order to detect drilling risks in real time. Besides that, the validity and reliability of the method were verified by experiments. Laboratory experiments conducted on a small-scale test facility and drilling field experiments conducted in a real well had proved the good performance of this newly proposed drilling risks monitoring method.

中文翻译：

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一种结合井筒水力学模型和流数据驱动模型参数反演算法的实时钻井风险监测方法

摘要 自动及早期钻井风险检测是降低钻井成本、提高钻井效率的重要课题。本文考虑到钻井过程数据的固有性质，开发了一种新型的钻井风险监测方法，可以自动实时检测钻井风险。新提出的方法结合井筒水力学模型和流数据驱动的模型参数反演算法，实现钻井风险检测。通过深入分析几种钻井风险的共同响应特征，首先定义了压力损失因子和流量因子两个钻井风险指标。然后建立钻井风险监测模型，对压力和流量响应进行建模。在模型中，压力损失因子和流量因子是模型参数，需要使用流数据进行反演。最后，采用流数据驱动的模型参数反演算法估计压力损失因子和流量因子，以实时检测钻井风险。并通过实验验证了该方法的有效性和可靠性。在小型试验设备上进行的实验室试验和在实井中进行的钻井现场试验证明了这种新提出的钻井风险监测方法的良好性能。采用流数据驱动的模型参数反演算法估计压力损失因子和流量因子，实时检测钻井风险。并通过实验验证了该方法的有效性和可靠性。在小型试验设备上进行的实验室试验和在实井中进行的钻井现场试验证明了这种新提出的钻井风险监测方法的良好性能。采用流数据驱动的模型参数反演算法估计压力损失因子和流量因子，实时检测钻井风险。并通过实验验证了该方法的有效性和可靠性。在小型试验设备上进行的实验室试验和在实井中进行的钻井现场试验证明了这种新提出的钻井风险监测方法的良好性能。