上海交通大学学报

上海交通大学学报 >

2024 , Vol. 58 >Issue 11: 1698 - 1706

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2022.455

船舶海洋与建筑工程

系统不确定的深海钻井立管解脱反冲智能控制

  • 王向磊 ,
  • 刘秀全 ,
  • 刘兆伟 ,
  • 畅元江 ,
  • 陈国明
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  • 1.中国石油大学(华东) 海洋油气装备与安全技术研究中心,山东 青岛 266580
    2.中国石油大学(华东) 海洋物探及勘探开发装备国家工程研究中心,山东 青岛 266580
王向磊(1994—),博士生,从事海洋油气管柱动力学研究.
刘秀全,副教授,博士生导师,电话(Tel.):0532-86983300;E-mail:lxqmcae@163.com.

收稿日期: 2022-11-09

  修回日期: 2023-05-12

  录用日期: 2023-05-16

  网络出版日期: 2023-06-01

基金资助

国家自然科学基金(52271300);国家自然科学基金(51809279)

收起

Intelligent Control for Deepwater Drilling Riser Disconnection and Recoil Considering System Uncertainty

  • WANG Xianglei ,
  • LIU Xiuquan ,
  • LIU Zhaowei ,
  • CHANG Yuanjiang ,
  • CHEN Guoming
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  • 1. Centre for Offshore Engineering and Safety Technology, China University of Petroleum, Qingdao 266580, Shandong, China
    2. National Engineering Research Center for Marine Geophysical Prospecting and Exploration Equipment, China University of Petroleum, Qingdao 266580, Shandong, China

Received date: 2022-11-09

  Revised date: 2023-05-12

  Accepted date: 2023-05-16

  Online published: 2023-06-01

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摘要

深海钻井立管系统紧急解脱后的反冲控制是深海油气钻探中的必备技术,但系统动力参数在多方面具有不确定性与难测性,给实际反冲控制带来了严峻挑战.建立系统模型不确定条件下的立管反冲智能自适应控制方法,通过反冲控制名义状态空间方程与闭环系统稳定性推导考虑模型不确定的修正控制输入,采用径向基(RBF)神经网络逼近模型不确定部分并选取满足李雅普诺夫(Lyapunov)稳定的权值自适应律,实现控制输入中对不确定部分的动态补偿.结果表明:该方法适用于实际反冲控制阀有调控速度限制的情况;张紧器刚度、阻尼、钻井液下泄摩擦力及立管浮力载荷等不确定性对反冲动力响应及控制效果均有一定影响;无论系统参数是否精确,经过RBF自适应控制后都降低了反冲初期振荡高度且降低了反冲触底风险.研究成果可有效解决工程背景下缺乏准确系统参数的立管反冲控制难题.

关键词： 钻井立管; 紧急解脱; 反冲控制; 参数不确定; 径向基神经网络

本文引用格式

王向磊 , 刘秀全 , 刘兆伟 , 畅元江 , 陈国明 . 系统不确定的深海钻井立管解脱反冲智能控制[J]. 上海交通大学学报, 2024 , 58(11) : 1698 -1706 . DOI: 10.16183/j.cnki.jsjtu.2022.455

Abstract

Recoil control for deepwater drilling riser system after emergency disconnection is a necessary technique in deepwater oil exploration. However, some dynamic parameters of the riser system are uncertain and difficult to measure, which poses severe challenges to the riser recoil control. Therefore, an intelligent riser recoil adaptive control method considering system uncertainties is established. Based on the nominal state-space expression of recoil control and closed-loop system stability, the modified control input considering model uncertainties is derived. The radial basis function (RBF) neural network is adopted to approximate model uncertainties, and the weight adaptive law satisfying Lyapunov stability is selected to realize dynamic compensation of uncertainties in control inputs. The results show that the proposed method is applicable to the actual recoil control valve with adjustment speed limit. The uncertainties of tensioner stiffness, damping, mud discharge friction, and riser buoyancy loads have a certain effect on recoil dynamic response and control performance. The RBF adaptive control method can effectively reduce the initial recoil oscillation height and reduce the risk of recoil bottoming. The findings can effectively solve the problem of recoil control without accurate system parameters in the engineering background.

Key words： drilling riser; emergency disconnection; recoil control; parameter uncertainty; radial basis function (RBF) neural network

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