基于MFAC潜油泵生产过程控制

发布时间：2019-01-09 09:19

【摘要】：随着我国经济的飞速发展,各行各业对能源消耗的需求量也日益增多,倍受全世界青睐的石油作为“工业血液”是诸多加工制造的重要原料,石油作为一种天然资源和全球战略性物资,在各国综合国力竞争中占有举足轻重的地位。我国虽然石油储存量十分充足,但随着开采强度逐年加大以及时间的推移,现有地下储油量已经不足以实现自喷,加之井下高温高压恶劣环境对开采控制系统的影响,自然状态下已无法满足经济的需求。因此实现原油开采控制过程的改进,增强提高开采过程控制系统的抗干扰性,实现采油的精细控制,已经成为国内外研究专家共同关注的问题。首先,本文介绍基于无模型自适应控制潜油泵的闭环系统,考虑潜油电泵工作在复杂多变的高温高压环境而提出的一种数据驱动控制办法,仅靠控制器的输入输出数据而不受被控对象数学模型限制的无模型自适应控制。潜油电泵作为原油开采的核心设备,主要由潜油异步电机和离心泵构成,因其具有耐高温高压、耐腐蚀、高扬程以及可远距离生产的优点被广泛使用。然而温度和压力的动态变化是影响潜油泵整个开采控制过程最主要因素,为了提高生产效率以及系统的稳定性,设计基于无模型控制潜油泵闭环控制系统,通过验证井下压力参数变化对采油系统的影响,使系统保证节能高效采油,稳定安全的作业。其次,从提高无模型自适应控制鲁棒性的角度出发。尽管无模型自适应控制器在潜油泵采油中得到较好应用,但现有系统的实际问题是井下噪声和数据丢失。虽然无需知道受控系统的数学模型信息,但控制器输入数据的不确定性的影响将直接导致系统输出数据的误差及波形的不真实。为了提高系统的稳定性和鲁棒性,本文从模拟加入量测噪声和模拟数据丢失两个方面进行研究,对已有无模型自适应控制潜油泵系统进行改进。最后,使用Matlab/Simulink仿真软件,分别对带有微分跟踪器的无模型自适应控制潜油泵系统和带有数据丢失补偿控制潜油泵的系统进行仿真,并与未改进之前的结果特性进行比较,对比结果表明具有很好的提升,控制系统具有更好的动态鲁棒性,实现改进的目标。
[Abstract]:With the rapid development of China's economy, the demand for energy consumption in various industries is increasing day by day. Oil, which is favored by the world as an "industrial blood", is an important raw material for processing and manufacturing. As a natural resource and global strategic material, petroleum plays an important role in the competition of national comprehensive national strength. Although the oil storage in our country is very sufficient, with the increase of exploitation intensity and the passage of time, the existing underground oil storage is not enough to realize self-injection, and the bad environment of underground high temperature and high pressure affects the exploitation control system. The natural state can no longer meet the needs of the economy. Therefore, it has become a common concern of domestic and foreign experts to improve the control process of crude oil production, enhance the anti-interference of the production process control system and realize the fine control of oil production. First of all, this paper introduces a closed loop system based on modelless adaptive control of submersible pump, which considers that the submersible electric pump works in a complex high-temperature and high-pressure environment and a data-driven control method is proposed. A modelless adaptive control based on the input and output data of the controller and not limited by the mathematical model of the controlled object. As the core equipment of crude oil production, submersible electric pump is mainly composed of submersible asynchronous motor and centrifugal pump. It has the advantages of high temperature and pressure resistance, corrosion resistance, high lift and long distance production. However, the dynamic change of temperature and pressure is the most important factor affecting the whole production control process of submersible pump. In order to improve the production efficiency and the stability of the system, the closed-loop control system of the submersible pump based on model-free control is designed. By verifying the influence of the change of downhole pressure parameters on the oil recovery system, the system ensures energy saving and efficient oil recovery and stable and safe operation. Secondly, from the point of view of improving the robustness of model-free adaptive control. Although the modelless adaptive controller is well applied in submersible pump, the actual problems of the existing system are downhole noise and data loss. Although there is no need to know the mathematical model information of the controlled system, the uncertainty of the input data of the controller will directly lead to the error of the system output data and the untruth of the waveform. In order to improve the stability and robustness of the system, the existing model-free adaptive control submersible pump system is improved from the aspects of analog measurement noise and simulated data loss. Finally, the simulation software Matlab/Simulink is used to simulate the model-free adaptive control submersible pump system with differential tracker and the submersible pump system with data loss compensation control respectively. The results show that the control system has better dynamic robustness and better performance.
【学位授予单位】：沈阳工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TE933.3

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