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Research on an Optimization Method for Injection-Production Parameters Based on an Improved Particle Swarm Optimization Algorithm

Author

Listed:
  • Yukun Dong

    (College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China)

  • Yu Zhang

    (College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China)

  • Fubin Liu

    (College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China)

  • Zhengjun Zhu

    (Research Institute of Exploration and Development, Tarim Oilfield Company, PetroChina, Korla 841000, China)

Abstract

The optimization of injection–production parameters is an important step in the design of gas injection development schemes, but there are many influencing factors and they are difficult to determine. To solve this problem, this paper optimizes injection-production parameters by combining an improved particle swarm optimization algorithm to study the relationship between injection-production parameters and the net present value. In the process of injection-production parameter optimization, the particle swarm optimization algorithm has shortcomings, such as being prone to fall into local extreme points and slow in convergence speed. Curve adaptive and simulated annealing particle swarm optimization algorithms are proposed to further improve the optimization ability of the particle swarm optimization algorithm. Taking the Tarim oil field as an example, in different stages, the production time, injection volume and flowing bottom hole pressure were used as input variables, and the optimal net present value was taken as the goal. The injection-production parameters were optimized by improving the particle swarm optimization algorithm. Compared with the particle swarm algorithm, the net present value of the improved scheme was increased by about 3.3%.

Suggested Citation

  • Yukun Dong & Yu Zhang & Fubin Liu & Zhengjun Zhu, 2022. "Research on an Optimization Method for Injection-Production Parameters Based on an Improved Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 15(8), pages 1-18, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:8:p:2889-:d:794105
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    References listed on IDEAS

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    1. Yuan Zhang & Yuan Di & Yang Shi & Jinghong Hu, 2018. "Cyclic CH 4 Injection for Enhanced Oil Recovery in the Eagle Ford Shale Reservoirs," Energies, MDPI, vol. 11(11), pages 1-15, November.
    2. Komboigo Charles & Naomitsu Urasaki & Tomonobu Senjyu & Mohammed Elsayed Lotfy & Lei Liu, 2018. "Robust Load Frequency Control Schemes in Power System Using Optimized PID and Model Predictive Controllers," Energies, MDPI, vol. 11(11), pages 1-18, November.
    3. Gejirifu De & Wangfeng Gao, 2018. "Forecasting China’s Natural Gas Consumption Based on AdaBoost-Particle Swarm Optimization-Extreme Learning Machine Integrated Learning Method," Energies, MDPI, vol. 11(11), pages 1-20, October.
    4. Hongyang He & Zhigang Lu & Xiaoqiang Guo & Changli Shi & Dongqiang Jia & Chao Chen & Josep M. Guerrero, 2022. "Optimized Control Strategy for Photovoltaic Hydrogen Generation System with Particle Swarm Algorithm," Energies, MDPI, vol. 15(4), pages 1-17, February.
    5. Yunqi Xiao & Yi Wang & Yanping Sun, 2018. "Reactive Power Optimal Control of a Wind Farm for Minimizing Collector System Losses," Energies, MDPI, vol. 11(11), pages 1-15, November.
    6. Adrian Eisenmann & Tim Streubel & Krzysztof Rudion, 2022. "Power Quality Mitigation via Smart Demand-Side Management Based on a Genetic Algorithm," Energies, MDPI, vol. 15(4), pages 1-24, February.
    7. Congcong Gong & Haisong Chen & Weixiong He & Zhanliang Zhang, 2017. "Improved multi-objective clustering algorithm using particle swarm optimization," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-19, December.
    8. Chi Yao & Yulong Shao & Jianhua Yang, 2018. "Numerical Investigation on the Influence of Areal Flow on EGS Thermal Exploitation Based on the 3-D T-H Single Fracture Model," Energies, MDPI, vol. 11(11), pages 1-19, November.
    9. Ji-Qing Qu & Qi-Lin Xu & Ke-Xue Sun, 2022. "Optimization of Indoor Luminaire Layout for General Lighting Scheme Using Improved Particle Swarm Optimization," Energies, MDPI, vol. 15(4), pages 1-18, February.
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