Special Issue on AI-Enabled CCUS and New Energy Integration: Rock Mechanics-Supported Carbon-Neutral Geo-Energy Systems

The global transition to carbon neutrality has prompted the oil and gas industry to accelerate decarbonization. The deep integration of carbon capture, utilization and storage (CCUS) with renewable energy, hydrogen production, and digital intelligence has emerged as the only technically and economically viable pathway to transform traditional fossil energy systems into carbon-neutral ones.

In particular, rock mechanics governs fluid-rock interactions, multiphase flow, and geomechanical responses critical to the safety and scalability of CCUS and energy storage. However, traditional rock mechanics research faces limitations in addressing the complex multiscale, multiphysics, and highly nonlinear processes inherent in CCUS-new energy hybrid systems. Furthermore, despite the integration of AI and rock mechanics, the development of CCUS-new energy systems remain in its infancy.

This special issue aims to establish a new benchmark for interdisciplinary research at the intersection of rock mechanics, artificial intelligence, and carbon management.

Topics of Interest

Topics of interest include, but are not limited to:

• AI-assisted rock mechanics property characterization and evolution under CO₂-rock and hydrogen-rock interactions
• Machine learning-driven multiscale modeling of HMCT coupling processes in porous and fractured rock masses
• Digital rock mechanics enhanced by computer vision and deep learning for CCUS and geological energy storage
• AI-driven intelligent evaluation of reservoir injectivity, caprock sealing capacity, and long-term containment security
• Real-time monitoring and early warning of induced seismicity and fault reactivation in CCUS projects using data fusion techniques
• Geomechanical and rock mechanics optimization of CO₂-EOR and integrated storage systems in oil and gas fields
• Rock mechanics foundations and AI-enabled design of carbon-neutral oil and gas field development schemes
• Geological hydrogen storage: rock mechanics mechanisms, AI-driven site selection, and operational optimization
• Compressed air energy storage and underground pumped hydro storage: rock mechanics challenges and AI solutions
• Digital twin technology for intelligent operation and maintenance of CCUS and geological energy storage facilities
• Life cycle performance assessment of carbon-neutral oil and gas systems based on rock mechanics and AI
• Deep-sea CO₂ hydrate solidification storage: seabed rock mechanics and AI-assisted risk management
• Electrochemical CO₂ reduction and mineral carbonation: rock-fluid interface physics and machine learning optimization

Guest Editors

• Prof. Shuyu Sun, School of Mathematical Sciences, Tongji University, suns@tongji.edu.cn
• Prof. Tao Zhang, College of New Energy, China University of Petroleum (East China), tao.zhang@upc.edu.cn
• Prof. Li Ren, College of Architecture & Environment, Sichuan University, renli@scu.edu.cn