Tracking the multi‐well surface dynamometer card state for a sucker‐rod pump by using a particle filter. Issue 16 (10th September 2018)

• Record Type: Journal Article
• Authors: Wang, An; Gong, Guoliang; Shen, Rongxuan; Mao, Wenyu; Lu, Huaxiang; Wang, Ke; Wang, Junping
• Is Part Of: IET communications. Volume 12:Issue 16(2018)
• Page Start: 2058
• Page End: 2066
• Publication Date: 2018-09-10
• Subjects: particle filtering (numerical methods) -- oil technology -- rods (structures) -- dynamometers -- state‐space methods -- human factors -- fuel pumps; long‐term state tracking -- sucker‐rod pump -- particle filter -- nonlinear sucker‐rod pumping system -- surface dynamometer card estimation algorithm -- polished rod load -- pump stroke -- load sensor -- drift problems -- local characteristic correlation method -- state‐space model -- pumping unit -- time importance density -- current measurement information -- multiwell surface dynamometer card state tracking; Telecommunication systems -- Periodicals; Speech processing systems -- Periodicals; 621.38205
• DOI: 10.1049/iet-com.2018.5331
• Languages: English
• ISSNs: 1751-8628

Abstract

Abstract : For a non‐linear sucker‐rod pumping system, a surface dynamometer card estimation algorithm based on a particle filter is presented. The dynamometer card is a plot of the polished rod load at various positions of a pump stroke. Since the polished rod load measured by a load sensor is frequently affected by drift problems, a local characteristic correlation method is proposed while building the state‐space model for the pumping unit. The local characteristic correlation method makes the system insensitive to load drift problems. Moreover, the prior data recorded from different wells are used to construct the importance density. To make the k ‐time importance density closer to the real posterior distribution, current measurement information is used. The performance of the proposed algorithm is evaluated on the actual operating data of a Xinjiang oil field containing typical daily production activities that can cause sudden system state changes. The results show that the proposed algorithm can adapt to sudden changes of the underground environment caused by various human factors, and it can provide robust estimation for multi‐well long‐term state tracking.