Study and Application of Multi-parameter Early Warning Model for Gas Wells

doi:10.11885/j.issn.1674-5086.2020.06.12.01

Abstract

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

CLC Number:

TE345

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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URL: http://journal15.magtechjournal.com/Jwk_xnzk/EN/10.11885/j.issn.1674-5086.2020.06.12.01

http://journal15.magtechjournal.com/Jwk_xnzk/EN/Y2020/V42/I6/133

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