基于知识图谱的潜油电泵井故障诊断

潜油电泵井系统是油田开采重要工具,具有排量大、扬程高与作业环境灵活多变等优点.为了降低潜油电泵井系统故障危害,需要对其发生故障部件进行快速精确定位并维修.本文提出一种基于知识图谱的潜油电泵井故障诊断方法.采用改进BiLSTM-CRF实体识别算法与BERT关系抽取算法提取故障数据中的专家知识,构建潜油电泵井故障诊断领域知识图谱;利用构建知识图谱搭建以故障征兆为初始节点的贝叶斯推理网络,利用历史故障数据与条件概率解耦的计算方式推理出故障原因.本文通过故障诊断真实案例进行方法验证.

Fault Diagnosis for Electric Submersible Pump Well Based on Knowledge Graph

The electric submersible pump well system is an important tool for oilfield exploitation owing to its advantages of large displacement, high head, and flexible operating environment. Reducing the hazards of the faults in the electric submersible pump well system requires the fault components to be quickly and precisely located and repaired. This study proposes a knowledge graph-based fault diagnosis method for electric submersible pump wells. The improved bi-directional long short-term memory-conditional random field (BiLSTM-CRF) entity identification algorithm and the bidirectional encoder representations from transformers (BERT) relation extraction algorithm are used to extract expert knowledge from fault data and then construct a knowledge graph in the field of fault diagnosis of electric submersible pump wells; a Bayesian inference network with fault signs as initial nodes is built with the constructed knowledge graph, and the cause of the fault is inferred by utilizing historical fault data and the calculation method of decoupling conditional probabilities. The proposed method is validated by real fault diagnosis cases.