Abstract In deep geological drilling processes, the geological environment becomes more complex with the increasing of the drilling depth; consequently, the risks of downhole incidents get higher. If not discovered in time, these downhole incidents may develop to serious drilling accidents, causing significant financial and environmental losses. In this paper, a new method is proposed to diagnose downhole incidents by extracting trend features in multi-time scales and establishing a probabilistic neural network based diagnosis model. There are two major contributions: First, a feature extraction method is proposed to produce trend features from original process signals in different time scales; Second, an incident diagnosis method based on a broad probabilistic neural network is proposed to achieve better diagnosis performance in an expanded input space. Industrial case studies are presented to demonstrate the effectiveness and practicability of the proposed method. Results show that the proposed method has superior performance in diagnosing downhole incidents for geological drilling processes.

中文翻译：

---

使用多时间尺度特征提取和概率神经网络诊断地质钻井过程的井下事故

摘要 在深部地质钻井过程中，随着钻井深度的增加，地质环境变得更加复杂；因此，井下事故的风险更高。如果不及时发现，这些井下事故可能发展为严重的钻井事故，造成重大的经济和环境损失。本文提出了一种通过提取多时间尺度的趋势特征并建立基于概率神经网络的诊断模型来诊断井下事故的新方法。主要贡献有两个：第一，提出了一种特征提取方法，从不同时间尺度的原始过程信号中产生趋势特征；第二，提出了一种基于广义概率神经网络的事件诊断方法，以在扩展的输入空间中获得更好的诊断性能。提出了工业案例研究，以证明所提出方法的有效性和实用性。结果表明，该方法在地质钻井过程中的井下事故诊断中具有优越的性能。