摘要：With the development of digitalization and intelligence in the oil and gas industry, fast and accurate fault diagnosis is getting more attention day by day and the Bayesian network is widely used as an effective method in this field. However, oilfield transfer stations are highly complex, making it difficult to capture the dynamic response symptoms of fault modes. Meanwhile, existing Bayesian networks fail to consider the locational characteristics of equipment parameters during the modeling process, weakening the interpretability of fault propagation. In addition, the inadequate quantification of risk fluctuations between thresholds by hard evidence leads to low model adaptability. To overcome these limitations, we propose a soft evidence-enhanced object-oriented Bayesian network (OOBN). First, a simulation model is developed to generate accurate and reliable dynamic fault characteristics. Second, we incorporate the locations of equipment parameters into the Bayesian network, enhancing the interpretability of fault propagation. Then, soft evidence is used to quantify risk changes through probabilistic mapping, thereby reducing the misdiagnosis in the Bayesian network. Finally, the proposed Bayesian network is compared with Bayesian methods based on hard evidence and deep learning models and validated through multiple case studies, fully demonstrating the accuracy and robustness of the proposed model. ? 2025 Society of Petroleum Engineers.

ISSN号：1086-055X

卷、期、页：v 30,n 10,p6294-6312

发表日期：2025-10-01

期刊分区（SCI为中科院分区）：三区

收录情况：SCI（科学引文索引印刷版）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称：SPE Journal

参与作者：张宣伟

通讯作者：刘大千,魏生远,沈思亨,闪向营

第一作者：宋尚飞,史博会,宫敬

论文类型：期刊论文

论文概要：刘大千,宋尚飞,魏生远,张宣伟,沈思亨,闪向营,史博会,宫敬，Soft Evidence-Enhanced Object-Oriented Bayesian Network for Process Fault Diagnosis: A Case Study in Oilfield Transfer Station System，SPE Journal，2025，v 30,n 10,p6294-6312

论文题目：Soft Evidence-Enhanced Object-Oriented Bayesian Network for Process Fault Diagnosis: A Case Study in Oilfield Transfer Station System