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基于综合性能指标的控压钻井过程多变量协调控制
孟卓然,徐宝昌,罗雄麟
(中国石油大学(北京)信息科学与工程学院,北京 102249)
摘要:
针对井底压力控制精度低且钻进时效差等关键技术瓶颈,提出一种综合多性能指标的控压钻井过程协调控制方法。分析环空压力预测模型、钻柱系统集总参数模型、机械钻速模型等不同的单一过程模型,建立适合控制系统设计的控压钻井过程全局模型;基于先进控制理论,充分考虑回压、泵流量、转速、钻压等不同操控变量对井底压力与机械钻速的影响,提出稳定井底压力、提高机械钻速并减弱黏滑振动幅值的非线性模型预测控制方法,并从控制效果、抗扰动鲁棒性等方面对比非线性模型预测控制与常规比例积分微分(PID)方法的差异性。结果表明:在模型发生扰动时常规PID方法无法保证稳定控制;操控变量的选取影响在线优化时间与控制效果,相比于PID控制器,以井底压差、机械钻速为被控变量,回压、转速、泵流量三者为操控变量的非线性模型预测控制器在不停止钻进的情况下,泵流量辅助回压与转速更加有利于快速抑制气侵,同时提升机械钻速,保证钻井要求。
关键词:  复杂地层  井底压力  机械钻速  多源变量  非线性模型预测控制
DOI:10.3969/j.issn.1673-5005.2022.02.020
分类号::TP 273.1
文献标识码:A
基金项目:国家重点研发计划(2019YFA0708304);中国石油天然气集团有限公司-中国石油大学(北京)战略合作科技专项(ZLZX2020-03)
Multivariable coordinated control of managed pressure drilling process based on comprehensive performance index
MENG Zhuoran, XU Baochang, LUO Xionglin
(College of Information Science and Engineering in China University of Petroleum(Beijing), Beijing 102249, China)
Abstract:
In view of the key technical bottlenecks, such as low accuracy of bottom hole pressure control and low drilling efficiency, a coordinated control method based on multiple performance index was proposed. Different single process models, such as the annular pressure prediction model, the lumped parameter model for drill string system, and the rate of penetration model were analyzed, and a global model of managed pressure drilling suitable for the design of control system was established. Based on the theory of advanced control, a nonlinear model predictive control method was proposed to stabilize the bottom hole pressure, improve the rate of penetration, and reduce the amplitude of stick-slip vibration. The method fully considers the impacts of different control variables such as back pressure, pump flow rate, rotation speed and weight on bit on the bottom hole pressure and rate of penetration. The differences of the nonlinear model predictive control and the conventional proportional integral differential( PID) method were compared from including control effect and anti-disturbance robustness. The results show that, the PID control algorithm cannot guarantee stable results when the model is disturbed, and selection of control variables impacts the online optimization time and control effect. In comparison, the nonlinear model predictive controller takes the bottom hole pressure difference and rate of penetration as controlled variables, and back pressure, rotation speed and pump flow rate as manipulated variables. Without stopping drilling, the pump flow rate auxiliary back pressure and rotation speed are conducive to inhibition of gas kick more rapidly, which improves the rate of penetration to ensure the requirements of drilling.
Key words:  complex formation  bottom hole pressure  rate of penetration  multi-source variables  nonlinear model predictive control
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