基于贝叶斯网络的钻井现场数据采集器故障诊断方法研究
Study on Bayesian Network Based Fault Diagnosis Method for Drilling Site Data Collector
钟剑 1蒋味廷 2王勇 1宫玉明 1蒋雨松 1程小峻1
作者信息
- 1. 中国石油集团测井有限公司制造公司,陕西西安 710077
- 2. 重庆大学机械与运载工程学院,重庆 400044
- 折叠
摘要
针对钻井现场数据采集器的故障特点,利用相关函数分析法和先验知识确定故障节点以及故障因果关系,初步构建贝叶斯网络(BN)结构,使用K2学习算法和期望最大算法(EM)分别对BN结构进行优化和参数学习,得到井场数据采集器故障诊断模型,并基于历史数据对该模型加以验证.在此基础上综合考虑井场数据采集器发生故障的多方面影响因素,确认温度和湿度为设备故障的影响因素并建立井场数据采集器故障预测模型,并仿真分析了采集器故障影响机理.
Abstract
Focusing on the fault characteristics of data collector in drill well site,fault nodes and causal relationships are determined with correlation function analysis and prior knowledge,a Bayesian networks(BN)structure is preliminarily constructed.BN structure is optimized and parameters is learned with K2 learning algorithm and expectation maximization algorithm(EM),respectively.The fault diagnosis model of drilling data collector is obtained,the model is validated with historical data.On the basis,with comprehensively consideration of multiple factors affecting the failure of drilling data collectors,temperature and humidity are confirmed as the affect factors,the prediction model for drilling data collectors is established,and the influence mechanism of collector fault is simulated and analyzed.
关键词
钻井工程/数据采集器/故障诊断/故障预测/贝叶斯网络Key words
drilling engineering/data collector/fault diagnosis/fault prediction/Bayesian network引用本文复制引用
基金项目
中国石油集团测井有限公司科技项目(C-CNLC2022-3A04)
出版年
2024
NETL
NSTL
万方数据