Low-permeability reservoirs are critical frontiers for oil and gas displacement, where CO2 water-alternating-gas (CO2-WAG) injection plays a pivotal role in both enhanced oil recovery (EOR) and carbon capture, utilization, and storage (CCUS). However, development performance is highly sensitive to injection parameter...
The production of sustainable gaseous fuels via anaerobic digestion (AD) is a promising pathway to decarbonize fossil-reliant heat and electricity systems. Despite their potential, the development and optimization of biogas and renewable natural gas (RNG) production systems remain constrained by the time-consuming natu...
This review systematically evaluates machine learning as a foundational paradigm for overcoming computational and scale-bridging challenges in geothermal energy and underground thermal storage, highlighting physics-informed machine learning (PIML) and hybrid architectures that embed conservation laws as strict constrai...
: Hydro-mechanical (HM) simulations of porous-media systems—such as those used in geotechnical engineering, groundwater flow, formation consolidation, and underground-structure safety assessment—become computationally expensive when large material-and load-parameter spaces must be explored for design optimization, unce...
The growing need for flexible operation and reliable steam supply challenges coal-fired boilers to maintain an operating economy through fixed or single-variable combustion adjustments. This study develops a condition-aware, data-driven framework for coordinated combustion optimization. Historical stable-operation sa...
Parameter sensitivity analysis is a critical step in the numerical simulation and parameter design of CCUS models for high-temperature oil reservoirs. Owing to the complexity of these models, a single simulation run is computationally expensive, and sensitivity analysis typically requires numerous model evaluations, ma...
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