Application of Machine Learning Algorithms to Predict Gas Sorption Capacity in Heterogeneous Porous Material
Shale gas is a clean and effective energy source that plays a big part in the transition from high-carbon to low-carbon energy, serving as a link for the growth of low-carbon energy in the future. Since shale rock is a heterogeneous porous material, the best production strategy is determined by a pr...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Resources |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9276/14/5/80 |
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| Summary: | Shale gas is a clean and effective energy source that plays a big part in the transition from high-carbon to low-carbon energy, serving as a link for the growth of low-carbon energy in the future. Since shale rock is a heterogeneous porous material, the best production strategy is determined by a precise assessment of geological gas-in-place. Therefore, the economic and technical foresight of the production operations depends on the estimation of the adsorbed gas amount in shale resources. The isotherm curves of shale gas derived in this study were classified as type 1 isotherms, which indicates the presence of micropores in these samples. In this work, XGBoost (extreme gradient boosting) and ANN (artificial neural network) optimized with ABC (artificial bee colony) and PSO (particle swarm optimization) have been proposed to learn and then predict the methane sorption capacity (MSC) in shale based on total organic carbon (TOC), temperature, pressure, and moisture as input variables, with the gas adsorption amount of shale as the output. Statistical and graphical methods were used to compare the experimental results with the expected values. By comparison, the current work’s ANN-ABC and ANN-PSO models outperform all previous studies with higher R<sup>2</sup> values (0.9913 and 0.9954) and lower RMSE scores (0.0457 and 0.0420), respectively, indicating improved predictive accuracy and generalization ability. The findings demonstrate that, in comparison to earlier models, the suggested models provide an exceptional prediction of the adsorbed gas amount in a heterogeneous porous medium. With additional data available, it may be easily updated for wider applications. Overall, this paper shows that machine learning can be used to forecast shale gas adsorption, and a well-trained model may be incorporated into a large numerical framework to optimize shale gas production curves. |
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| ISSN: | 2079-9276 |