Guest Editor(s)
Prof. Haibin Chang
Email: changhaibin@cumtb.edu.cn
Affiliation: School of Energy and Mining Engineering, China University of Mining and Technology-Beijing, Beijing, China
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Research Interests: underground gas storage, unconventional natural gas development, machine learning methods
Summary
The global energy landscape is rapidly evolving, yet natural gas remains a key fundamental fuel for the transition toward a low-carbon future. However, the efficient, safe, and sustainable utilization of natural gas resources faces increasingly complex challenges. These include the development of unconventional reserves (e.g., shale gas, coalbed methane, and natural gas hydrates), the optimization of large-scale transportation networks, and the strategic operation of underground storage facilities. Addressing these challenges requires a paradigm shift from traditional methods to a new generation of approaches that integrate deep physical understanding with cutting-edge intelligent technologies.
This Special Issue aims to provide a high-level platform for sharing innovations and insights into this field. It considers new research advances in physical mechanisms and intelligent technologies involved in natural gas development, transportation, and storage processes.
Topics of interest include, but are not limited to:
1. Seepage Mechanisms in Conventional/Unconventional Gas Reservoir Development
2. Optimization Methods for Natural Gas Transportation and Distribution Systems
3. Mechanisms and Intelligent Operation Methods of Underground Natural Gas Storage
4. Integrated Modelling of Geological Formation-Wellbore-Pipeline Network System
5. Methods for Monitoring, Risk Warning, and Intelligent Decision-Making in Geological Formations, Wellbores, and Pipeline Networks
6. Applications of Artificial Intelligence Methods in Natural Gas Development, Transportation, and Storage
7. Market Mechanism Analysis and Policy Research for Natural Gas
Keywords
natural gas development, natural gas transportation, natural gas storage, physical mechanisms, mathematical modeling, intelligent technologies
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