As drilling technology advances and operations extend into more complex geological environments, evaluating drilling risks has become increasingly complex, challenging the effectiveness of traditional methods. The novel physics-guided spatial-temporal data mining method that integrates external and internal causal attention mechanisms for drilling risk evaluation is proposed to address this issue. Firstly, a risk calibration method based on spatial-temporal sequence clustering is designed. This method dynamically calibrates drilling risks by mining subtle changes in sign data during drilling. Secondly, an expert experience extraction method based on the correlation of drilling risk signs and a fuzzy inference system is established. Kendall's tau is used to quantify the correlation between drilling risk signs. The fuzzy inference system is employed to convert fuzzy and difficult-to-quantify expert experience into computable and interpretable rules. In order to improve the flexibility and adaptability of the fuzzy inference system, an expert experience rules base is also constructed. Subsequently, a spatial-temporal data mining model integrating both external and internal causal attention mechanisms (STMIEICAM) is constructed. The external causal attention mechanism (ECAM) quantified the correlation between signs and risk. The internal causal attention mechanism (ICAM) improved the model's ability to capture and quantify the features of spatial-temporal sequences. Finally, the physical knowledge from the fuzzy inference system and well-site is embedded into the STMIEICAM model, forming a physics-guided spatial-temporal data mining model integrating both external and internal causal attention mechanisms (PG-STMIEICAM) that enables graded evaluation of drilling risks. The proposed method was applied to overflow risk evaluation in an oil field to validate its effectiveness. The results demonstrate that the method not only excels in uncovering hidden relationships within the data but also integrates expert knowledge, achieving accurate evaluation of drilling risks.

中文翻译：

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一种新型物理引导的时空数据挖掘方法，具有外部和内部因果关系，用于钻井风险评估


随着钻井技术的进步和作业扩展到更复杂的地质环境，评估钻井风险变得越来越复杂，这对传统方法的有效性提出了挑战。针对该问题，该文提出一种融合外部和内部因果注意力机制的新型物理导向时空数据挖掘方法，用于钻井风险评估。首先，设计了一种基于时空序列聚类的风险校准方法;这种方法通过挖掘钻孔过程中标志数据的细微变化来动态校准钻孔风险。其次，建立了基于钻井风险征象相关性与模糊推理系统的专家经验提取方法;Kendall 的 tau 值用于量化钻井风险信号之间的相关性。采用模糊推理系统将模糊和难以量化的专家经验转化为可计算和可解释的规则。为了提高模糊推理系统的灵活性和适应性，还构建了专家体验规则库。随后，构建了一个整合外部和内部因果注意力机制的时空数据挖掘模型 （STMIEICAM）。外部因果注意力机制 （ECAM） 量化了迹象与风险之间的相关性。内部因果注意力机制 （ICAM） 提高了模型捕获和量化时空序列特征的能力。最后，将来自模糊推理系统和井场的物理知识嵌入到 STMIEICAM 模型中，形成一个物理引导的时空数据挖掘模型 （PG-STMIEICAM），该模型集成了外部和内部因果注意力机制 （PG-STMIEICAM），能够对钻井风险进行分级评估。 将所提出的方法应用于油田溢流风险评估，以验证其有效性。结果表明，该方法不仅擅长揭示数据中隐藏的关系，而且整合了专业知识，实现了对钻井风险的准确评估。