• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2017, Vol. 53 ›› Issue (13): 159-169.doi: 10.3901/JME.2017.13.159

• 数字化设计与制造 • 上一篇    下一篇

基于演化模型偏好多目标优化的智能采油辅助决策支持

辜小花1,2, 王坎3, 李燕4, 高论1, 李太福1, 周伟1   

  1. 1. 重庆科技学院电气与信息工程学院 重庆 401331
    , 2. 四川理工学院人工智能重点实验室 自贡 643000
    , 3. 西安石油大学电子工程学院 西安 710065
    , 4. 新疆华隆油田科技股份有限公司 新疆 834000
  • 出版日期:2017-07-05 发布日期:2017-07-05
  • 作者简介:

    辜小花(通信作者),女,1982年出生,副教授,博士,硕士研究生导师。主要研究方向为智能油气田、复杂系统建模与优化。

    E-mail:183219122@qq.com

  • 基金资助:
    * 国家自然科学基金(51375520, 51404051)、重庆市基础与前沿研究计划重点(cstc2015jcyjBX0089)、重庆市教委科学技术研究(KJ1401309, KJ1501304)和重庆科技学院校内科研基金(CK2016Z16)资助项目; 20160830收到初稿,20170319收到修改稿;

The Intelligent Oil Extraction Auxiliary Decision Based on Evolution Model and Preference Driven Multi-objective Optimization

GU Xiaohua1,2, WANG Kan3, LI Yan4, GAO Lun1, LI Taifu1, ZHOU Wei1   

  1. 1. School of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing 401331
    , 2. Key Laboratory of Artificial Intelligence, Sichuan University of Science & Engineering, Zigong, 643000
    , 3. Institute of Electronic Engineering, Xi’an Shiyou University, Xi’an 710065
    , 4. Xinjiang hualong oilfield technology Co. Ltd., Xinjiang 834000
  • Online:2017-07-05 Published:2017-07-05

摘要:

通过智能采油系统自主分析与决策获取油田机采过程最佳决策参数,对解决机采系统效率低、能耗大等问题具有重要意义。受机械、地层、人为等不确定因素影响,智能采油系统难以构建生产参数、环境变量与系统性能、设定生产方式之间的机理关系并优化决策。为此,提出基于动态演化建模的偏好多目标优化方法,以实现采油系统的自主决策。利用无迹卡尔曼滤波神经网络(Unscented Kalman filter neural network,UKFNN)挖掘机采系统潜在规律,建立其动态模型;构建产液量偏好多目标优化目标函数,并利用非改进支配排序遗传算法(Non-dominated sorting genetic algorithm 2,NSGA2)获取相应的最佳决策参数。某油田试验结果表明:该方法使得系统日耗电量降低15.87%,系统效率提高4.9%。可见,所提方法可行且有效。

关键词: 动态模型, 决策参数, 偏好多目标优化, 智能采油系统

Abstract:

Obtaining the optimal decision parameters by intelligent production systems’ autonomous analysis and decision has significant meanings to deal with the low efficiency and high energy consumption in the oil extraction process. However, it is quite difficult to conduct and optimize the mechanism relationships among the operation parameters, the environment variables and the production mode settings, due to the mechanical, geological and artificial factors. Therefore, a novel autonomous decision method of oil extraction system by preference driven multi-objective optimization based on dynamic evolution models is proposed. The potential law of the pumping systems and then establish the dynamic model by unscented Kalman filter neural network (UKFNN) is found. The preference multi-objectives are constructed according to the actual production mode. The optimal decision parameters are obtained by improved non-dominated sorting genetic algorithm (NSGA2). The experimental results show that after the proposed optimization the energy consumptions of the system decrease 15.87%, as well as the system efficiency improves over 4.9%, which illustrate the feasibility and the effectiveness of the proposed method.

Key words: decision parameters, dynamic model, preference driven multi-objective optimization, intelligent oil extraction system