J Shanghai Jiaotong Univ Sci ›› 2026, Vol. 31 ›› Issue (2): 486-498.doi: 10.1007/s12204-024-2736-x

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低渗油井节能生产参数多目标优化方法

  

  1. 1. 西安建筑科技大学 机电工程学院,西安710055;2. 中国机械联合工程有限公司 智能装备工程公司,重庆400039;3. 中国地质大学(武汉) 机械工程与电子信息学院,武汉430074;4. 西安鹏瑞石油科技有限公司 设计研发中心,西安 710016
  • 收稿日期:2023-06-29 接受日期:2023-09-07 出版日期:2026-04-01 发布日期:2024-05-06

Multi-Objective Approach for Optimizing Production Parameters of Low-Permeability Oil Well to Enhance Energy Efficiency

刘沛津1,丁浩健1,晏东阳2,孙浩峰3,黄涛1,李杰4   

  1. 1. School of Mechanical and Electrical Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China; 2. Intelligent Equipment Engineering Company, China Machine China Union Engineering Co., Ltd., Chongqing 400039, China; 3. School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China; 4. Design and Development Center, Xi’an Pengrui Petroleum Technology Co., Ltd., Xi’an 710016, China
  • Received:2023-06-29 Accepted:2023-09-07 Online:2026-04-01 Published:2024-05-06

摘要: 针对低渗油井开采过程中生产参数随着油井工况变化的跟踪性能不足、匹配度低下,导致高能耗、低能效的实际问题,提出一种基于模型误差补偿的多目标生产参数优化方法,并依此制定低渗油井节能生产参数。首先,建立了低渗油井生产过程单井日产液量收益子模型与单井单位生产能耗成本子模型,并以此为基础,采用高斯混合模型方法对单井单位生产能耗成本子模型进行误差补偿,解决油井生产过程中的不确定信息对模型精度的影响问题,提高模型精度;其次,综合考虑模型决策变量与约束,建立了以最大化日产液量收益,最小化单位生产能耗成本的多目标优化模型;然后,采用非支配排序遗传算法求解所建多目标优化模型得到生产参数。最后,将求解结果中特征明显的解集作为生产参数,应用到长庆油田某采油厂低渗油井进行实际生产验证。结果表明:油井生产收益增大,且节能效果明显,验证了本文所建模型和优化算法的有效性。

关键词: 低渗油井, 生产控制参数, 模型误差补偿, 多目标优化, 非支配遗传算法

Abstract: Aiming at the practical problems of high energy consumption and low energy efficiency during the exploitation of low-permeability oil wells because of insufficient traceability and poor matching performance of production parameters, this paper proposes a multi-objective approach for optimizing production parameters of low-permeability oil well to enhance its energy efficiency. First, a sub-model of daily liquid production yield and a sub-model of unit production energy consumption cost for single low-permeability oil well were established, and the Gaussian mixture model method was employed to compensate for the errors in the sub-model of unit production energy consumption cost, to solve the problem of the influence of uncertain facts during the oil well exploitation and to improve the precision of the model. Second, a multi-objective optimization model was established by taking into account the decision variables and constraints of the model, to maximize the daily liquid production yield while minimizing the unit production energy consumption cost. Subsequently, the non-dominated sorting genetic algorithm was employed to solve the multi-objective optimization model and obtain the production parameters. Finally, the solution set with obvious features was taken as the production parameters and applied to the actual production verification of low-permeability oil wells in a certain oil production plant of the ChangQing Oilfield. The results showed an increase in oil well production yield, and a significant energy-saving effect, thereby verifying the effectiveness of the proposed model and optimization algorithm in this paper.

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