Modeling study of sandstone permeability under true triaxial stress based on backpropagation neural network, genetic programming, and multiple regression analysis

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{YU:2021:JNGSE,

• author = "Beichen Yu and Honggang Zhao and Jiabao Tian and Chao Liu and Zhenlong Song and Yubing Liu and Minghui Li",
• title = "Modeling study of sandstone permeability under true triaxial stress based on backpropagation neural network, genetic programming, and multiple regression analysis",
• journal = "Journal of Natural Gas Science and Engineering",
• volume = "86",
• pages = "103742",
• year = "2021",
• ISSN = "1875-5100",
• DOI = "doi:10.1016/j.jngse.2020.103742",
• URL = "https://www.sciencedirect.com/science/article/pii/S1875510020305965",
• keywords = "genetic algorithms, genetic programming, Artificial intelligence systems, Permeability, True triaxial stress, Pore pressure",
• abstract = "Permeability evolution of sandstone is of great significance in the development of tight sandstone gas reservoirs. Traditional laboratory tests have the disadvantages of high cost and long testing time. Therefore, the present study employed use artificial intelligence systems, i.e., backpropagation neural network (BPNN), genetic programming (GP), and multiple regression analysis to construct prediction models of sandstone permeability based on the coupling effect of true triaxial stress field and pore pressure. The results showed that the permeability prediction obtained from the systems fit well with the experimental data, and evidenced that permeability increased with pore pressure and decreased with increase in principal stress. Sensitivity analysis showed that the pore pressure has the greatest influence on sandstone permeability under different true triaxial stress. The effect of anisotropic principal stress on permeability exhibited ?1 > ?2 > ?3 under fixed pore pressure. Further assessment based on a combination of five evaluation indexes showed that the prediction accuracy of the BPNN model was better",

}

Genetic Programming entries for Beichen Yu Honggang Zhao Jiabao Tian Chao Liu Zhenlong Song Yubing Liu Minghui Li

Citations