| Autor: |
Yanwei Wang, Hongyang Chu, Xiaocong Lyu |
| Předmět: |
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| Zdroj: |
Advances in Geo-Energy Research; Sep2024, Vol. 13 Issue 3, p161-165, 5p |
| Abstrakt: |
Deep learning has been widely recognized in the field of CO2 geological utilization and storage applications. With the development of deep learning algorithms, intelligent models are gradually able to improve multi-source, multi-scale and multi-physicochemical mechanism barriers with high-fidelity solutions in practical applications. In this perspective, an overview of the traditional and state-of-the-art deep learning architectures involved in CO2 geological utilization and storage is outlined in terms of evolutionary trajectories. Meanwhile, the favorable directions and application scenarios of different deep learning algorithms for geo-energy intelligence modeling are summarized. Moreover, further insights into the future direction of deep learning burgeoning architectures in this field are provided. The physics-guided deep learning, explainable artificial intelligence, and generative artificial intelligence are expected to deliver more accurate solutions for information extraction and decision support within the CO2 geological utilization and storage communities. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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