Application of GA-BP neural network algorithm in killing well control system

被引:0
|
作者
Haibo Liang
Qi Wei
Dengyun Lu
Zhiling Li
机构
[1] Southwest Petroleum University,School of Mechanical Engineering
[2] CNPC Chuanqing Drilling Engineering Technology Research Institute,undefined
来源
关键词
Killing well; Bottom-hole pressure control; Control model; GA-BP neural network;
D O I
暂无
中图分类号
学科分类号
摘要
Killing operation is an effective measure to restore bottom-hole pressure balance after unbalanced bottom-hole pressure shut-in. In the traditional well killing operation, the opening of the hydraulic throttle valve is manually adjusted by the throttle control box, and the manual control has the problems of uncertainty and low control precision, which makes the stability control of well killing operation a difficult problem. This paper presents a feedback control model based on a large number of real-time bottom-hole data, historical data and GA-BP neural network prediction. Through the intelligent control of throttle valve opening in the process of well killing operation, the fast, accurate and stable self-feedback control of bottom-hole pressure prediction and prediction output is realized. The analysis results show that the control model predicted by GA-BP neural network can effectively adjust the throttle opening and realize the stable and effective control of bottom-hole pressure.
引用
收藏
页码:949 / 960
页数:11
相关论文
共 50 条
  • [41] A Novel GA-BP Neural Network for Wireless Diagnosis of Rolling Bearing
    Zhu, Zhiliang
    Xu, Xiaofeng
    Li, Lujia
    Dai, Yuxing
    Meng, Zhiqiang
    JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS, 2022, 31 (10)
  • [42] Adaptive switching median filter based on GA-BP neural network
    Ye, Xiaoling
    Dou, Yanyan
    Liu, Bo
    MATERIALS PROCESSING AND MANUFACTURING III, PTS 1-4, 2013, 753-755 : 2980 - 2984
  • [43] Integrality detection of pile foundation based on GA-BP neural network
    Rong, L. X.
    2008 PROCEEDINGS OF INFORMATION TECHNOLOGY AND ENVIRONMENTAL SYSTEM SCIENCES: ITESS 2008, VOL 3, 2008, : 79 - 83
  • [44] Temperature prediction and analysis based on improved GA-BP neural network
    Zhang, Ling
    Sun, Xiaoqi
    Gao, Shan
    AIMS ENVIRONMENTAL SCIENCE, 2022, 9 (05) : 735 - 753
  • [45] Prediction of Residents' Travel Modes Based on GA-BP Neural Network
    Kong, Yaoyao
    Liang, Yanping
    Xu, Jiajun
    CICTP 2020: ADVANCED TRANSPORTATION TECHNOLOGIES AND DEVELOPMENT-ENHANCING CONNECTIONS, 2020, : 157 - 166
  • [46] Measurement of Project Portfolio Benefits With a GA-BP Neural Network Group
    Bai, Libiao
    An, Yuqin
    Sun, Yichen
    IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, 2023, 71 : 4737 - 4749
  • [47] Evaluating of leakage current of insulators based on the GA-BP neural network
    Zhang Y.
    Wu Y.
    Zhao S.
    Tiedao Xuebao/Journal of the China Railway Society, 2016, 38 (05): : 46 - 52
  • [48] Hybrid Neural Network Based On GA-BP for Personal Credit Scoring
    Wang, Shulin
    Yin, Shuang
    Jiang, Minghui
    ICNC 2008: FOURTH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, VOL 3, PROCEEDINGS, 2008, : 209 - 214
  • [49] A Modeling Approach for Energy Saving Based on GA-BP Neural Network
    Li, Junke
    Guo, Bing
    Shen, Yan
    Li, Deguang
    Huang, Yanhui
    JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY, 2016, 11 (05) : 1289 - 1298
  • [50] GA-BP neural network modeling for project portfolio risk prediction
    Bai, Libiao
    Wei, Lan
    Zhang, Yipei
    Zheng, Kanyin
    Zhou, Xinyu
    JOURNAL OF ENTERPRISE INFORMATION MANAGEMENT, 2024, 37 (03) : 828 - 850