Evaluation of Hydraulic Fracturing in Coal Seam Based on Time Difference-Particle Swarm Optimization Algorithm

被引:1
|
作者
Qian, Yanan [1 ,2 ,3 ,4 ]
Li, Quangui [1 ,2 ,6 ]
Li, Wenxi [1 ,2 ]
Hu, Qianting [1 ,2 ]
Yan, Benzheng [5 ]
Yu, Changjun [1 ,2 ]
Peng, Shuyue [1 ,2 ]
机构
[1] Chongqing Univ, State Key Lab Coal Mine Disaster Dynam & Control, Chongqing 400044, Peoples R China
[2] Chongqing Univ, Sch Resources & Safety Engn, Chongqing 400044, Peoples R China
[3] Sapienza Univ Rome, Res Ctr Geol Risks CERI, Earth Sci Dept, I-00185 Rome, Italy
[4] Sapienza Univ Rome, Res Ctr Geol Risks CERI, I-00185 Rome, Italy
[5] Res Inst Henan Energy & Chem Ind Grp Co Ltd, Zhengzhou 450046, Peoples R China
[6] Anhui Univ Sci & Technol, State Key Lab Min Response & Disaster Prevent & Co, Huainan 232001, Peoples R China
基金
中国国家自然科学基金;
关键词
MICROSEISMIC SOURCE LOCATION; MINE; PERFORMANCE;
D O I
10.1021/acs.energyfuels.4c02105
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Hydraulic fracturing (HF) technology is one of the key technologies to improve the permeability of coal seams, and microseismic (MS) source location is an effective method for evaluating the extent and effectiveness of HF. In this study, a new MS source location method is proposed based on the objective function by introducing a random weighting strategy: the time difference-particle swarm optimization (TD-PSO) location method. In this method, random inertia weight values, optimal candidate solution, and average evaluation value models are introduced, and asynchronous dynamic learning factors are proposed to improve the particle population diversity and convergence ability, which effectively avoids the tendency of the algorithm to fall into a local optimum. The convergence performance and search efficiency of the TD-PSO algorithm were verified using nonlinear multimodal functions. Through a large-scale field simulation of mine MS location tests, the search stability of the TD-PSO algorithm in 3D space and the accuracy of the seismic source location under the same monitoring data and test environment are verified, and the localization error is only 14.74 m. By evaluating the range of HF in the coal seams at Baijigou Mine, the fracturing ranges of YL1-1#, YL1-2#, and YL1-3# boreholes are 105, 45, and 75 m on the X-axis, and the fracturing ranges are 100, 50, and 80 m on the Y-axis, respectively, and the fracturing effect of YL1-1# borehole is the best. From the distribution of SRV value, the fracturing sections No. 2 and No. 4 of YL1-1# borehole have the best fracturing effect, which are 11210.47 and 4119.31 m(3), respectively.
引用
收藏
页码:10955 / 10971
页数:17
相关论文
共 44 条
  • [21] Solving redundant inverse kinematics of CMOR based on chaos-driven particle swarm optimization algorithm
    Zhao, Fang
    Cheng, Yong
    Pan, Hongtao
    Cheng, Yang
    Zhang, Xi
    Wu, Bo
    Hu, Youmin
    FUSION ENGINEERING AND DESIGN, 2023, 192
  • [22] Optimization of a novel magnetic refrigerator based on the demagnetizing effect using a particle swarm-like algorithm
    Fernandes, C. R.
    Amaral, J. S.
    Almeida, R.
    Belo, J. H.
    Ventura, J. O.
    Silva, D. J.
    INTERNATIONAL JOURNAL OF REFRIGERATION, 2025, 172 : 134 - 146
  • [23] A dynamic multi-objective particle swarm optimization algorithm based on adversarial decomposition and neighborhood evolution
    Zheng, Jinhua
    Zhang, Zeyu
    Zou, Juan
    Yang, Shengxiang
    Ou, Junwei
    Hu, Yaru
    SWARM AND EVOLUTIONARY COMPUTATION, 2022, 69
  • [24] Integrated design optimization method for pavement structure and materials based on further development of finite element and particle swarm optimization algorithm
    Haoran, Zhu
    Zidong, Zhou
    Min, Wang
    Xin, Yu
    Yongxin, Wu
    Chen, Chen
    Jun, Qiao
    CONSTRUCTION AND BUILDING MATERIALS, 2024, 426
  • [25] Optimization of microchannel heat sink based on multi-objective particle swarm optimization algorithm for integrated circuit chips cooling
    Jiang, Meixia
    Pan, Zhongliang
    NUMERICAL HEAT TRANSFER PART B-FUNDAMENTALS, 2023, : 840 - 858
  • [26] Evaluation of a Particle Swarm Optimization Based Method for Optimal Location of Photovoltaic Grid-connected Systems
    Gomez, M.
    Jurado, F.
    Diaz, P.
    Ruiz-Reyes, N.
    ELECTRIC POWER COMPONENTS AND SYSTEMS, 2010, 38 (10) : 1123 - 1138
  • [27] Response Characteristics of Coal-Like Material Subjected to Repeated Hydraulic Fracturing: An Evaluation Based on Real-Time Monitoring of Water Injection Pressure and Roof Stress Distribution
    Zhang, Yongjiang
    Yuan, Benqing
    Niu, Xingang
    SHOCK AND VIBRATION, 2021, 2021
  • [28] Genetic Algorithm-Enabled Particle Swarm Optimization (PSOGA)-Based Task Scheduling in Cloud Computing Environment
    Agarwal, Mohit
    Srivastava, Gur Mauj Saran
    INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING, 2018, 17 (04) : 1237 - 1267
  • [29] A Decomposition-Based Unified Evolutionary Algorithm for Many-Objective Problems Using Particle Swarm Optimization
    Pan, Anqi
    Tian, Hongjun
    Wang, Lei
    Wu, Qidi
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2016, 2016
  • [30] Particle swarm optimization algorithm-based PI inverter controller for a grid-connected PV system
    Roslan, M. F.
    Al-Shetwi, Ali Q.
    Hannan, M. A.
    Ker, P. J.
    Zuhdi, A. W. M.
    PLOS ONE, 2020, 15 (12):