气井多参数联合预警模型研究与应用

doi:10.11885/j.issn.1674-5086.2020.06.12.01

摘要/Abstract

摘要： SF气田于2016年启动生产信息化现场建设，完成站场及气井信息化采集部署，实现数据实时上传和站场可视化。但在数据应用方面，依托固定阈值报警模式的有效报警率低，无法实现自动提示异常工况，需要人工辅助判断，判断耗时长，准确率低。为实现异常数据智能分析、报警自动分级推送，提升信息化条件下的工作效率和生产效益，2018年开始启动智能提升计划，通过自定义统计方式计算气井主要生产参数，形成相应算法，根据计算结果判断是否出现异常情况；通过组合多参数预警信息，形成多参数联合预警模型，并匹配工况经验库，按预设值推送异常情况和处置意见，实现联合预警。这种信息化气田新型管理手段，保证了气井、井站异常诊断和异常生产处置的及时性，全面提升"异常管理"效率。

关键词: 工况, 单参数, 多参数, 生产异常, 预警模型, 智能提升

Abstract: SF Gas Field started the construction of digital field in 2016, completed the information collection and deployment of stations and gas wells, and realized real-time data upload and station visualization. However, in data application, the effective alarm rate relying on the fixed threshold alarm mode is low, which cannot automatically prompt abnormal working conditions. It needs manual auxiliary judgment, which takes a long time and has low accuracy. In order to realize the intelligent analysis of abnormal data and automatic alarm grading, and improve the work efficiency and production efficiency under the condition of informatization, the intelligent improvement plan was launched in 2018, the main production parameters of the gas well are calculated by means of a custom statistical method, and the corresponding algorithm is formed to judge whether there is any abnormal situation according to the calculation results. By combining the multi-parameter warning information, the multiparameter joint warning model is formed, and the working condition experience database is matched. According to the preset value, the abnormal situation and disposal opinions are pushed to realize the joint warning. This new management method of informationized gas field ensures the timeliness of abnormal diagnosis and production disposal in gas wells and well stations, and comprehensively improves the efficiency of abnormal management.

Key words: working condition, single parameter, multi-parameter, abnormal production, early warning model, intelligent promotion

中图分类号:

TE345

王皓, 张立夫, 罗昊. 气井多参数联合预警模型研究与应用[J]. 西南石油大学学报(自然科学版), 2020, 42(6): 133-140.

WANG Hao, ZHANG Lifu, LUO Hao. Study and Application of Multi-parameter Early Warning Model for Gas Wells[J]. Journal of Southwest Petroleum University(Science & Technology Edition), 2020, 42(6): 133-140.

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