Data driven based deep learning for optimizing carbon storage and methane adsorption in unconventional shale gas reservoirs
| Parent link: | Journal of Environmental Chemical Engineering.— .— Amsterdam: Elsevier Science Publishing Company Inc. Vol. 13, iss. 3.— 2025.— Article number 116901, 15 p. |
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| Other Authors: | , , , , , , |
| Summary: | Shale gas is a critical component of the global energy supply, necessitating accurate gas-in-place evaluations for optimized extraction. In unconventional shale reservoirs, gas adsorption is the primary storage mechanism. This study introduces a novel one-dimensional Convolutional Neural Network (1D-CNN) to predict gas adsorption. The 1D-CNN model was compared with traditional machine learning models, including Random Forest (RF), Extra Trees (ET), and Gradient Boosting (GB). Using a dataset of 352 samples with key features—pressure, temperature, TOC, and moisture content—the 1D-CNN achieved superior predictive accuracy, with a mean absolute error (MAE) of 0.015 and R² of 0.9949, outperforming other models. Sensitivity analysis identified moisture content as the most influential factor affecting adsorption. Validation using unseen methane adsorption data demonstrated that the PIDL model generalizes well and produces physically consistent predictions. Overall, the PIDL-enhanced 1D-CNN model advances methane adsorption prediction by integrating domain-specific physical laws into a deep learning framework, facilitating more efficient shale gas extraction and improved carbon sequestration practices. This hybrid approach not only reduces experimental costs but also provides consistent and reliable results, offering a robust solution for real-world applications Текстовый файл |
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2025
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| Subjects: | |
| Online Access: | https://doi.org/10.1016/j.jece.2025.116901 |
| Format: | Electronic Book Chapter |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=680359 |