期刊
JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING
卷 76, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.jngse.2020.103204
关键词
Adsorption capacity of CO2 and CH4; Carbon storage in shales; Multicomponent gas isotherm; LSSVM; PSO
Carbon dioxide enhanced shale gas recovery depends strongly on adsorption properties of carbon dioxide and methane. In this work, Least Squares Support Vector Machine (LSSVM) optimized by Particle Swarm Optimization, has been proposed to learn and then predict adsorption capacity of methane and carbon dioxide from pure and binary gas mixtures in Jurassic shale samples from the Qaidam Basin in China based on input parameters pressure, temperature, gas composition and TOC. A literature dataset of 348 points was applied to train and validate the model. The predicted values were compared with the experimental data by statistical and graphical approaches. The coefficients of determination of carbon dioxide adsorption were calculated to 0.9990 and 0.9982 for training and validation datasets, respectively. For CH4 the numbers are 0.9980 and 0.9966. The model was extrapolating reasonable trends beyond measurement ranges. More extensive datasets are needed to properly parameterize the role of shale properties.
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