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Issue:2025 - Vol. 1 - Iss.1
Published:2025.12
Articles:6 articles
Advanced Energy Systems and Intelligent Geoscience
AESIG
An international academic journal dedicated to the intersection of energy engineering, fundamental physics, and advanced computational science, dedicated to providing a high-quality academic exchange platform for researchers, scholars, and industry experts. The journal covers a wide range of research results, technological advancements, theoretical discussions, and practical applications in energy physics experimentation and computational technologies, with a particular focus on the cutting-edge dynamics and develo… More
2025 Vol.1 Iss.1 (5 articles)
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Research Article / Original Article
Review of the Application of Upwind Generalized Finite Difference Method in Two-Phase Fluid-Solid Coupling of Porous Media
Abstract: Porous media oil-water two-phase fluid-solid coupling is critical for oil-gas exploitation efficiency and environmental protection, but traditional mesh-based methods (FDM, FEM, FVM) suffer from mesh generation difficulties, poor dynamic adaptability, and numerical dissipation. The Generalized Finite Difference Method (GFDM), a promising meshless approach
Keywords: Closed-loop optimization; History matching; Production optimization; Optimization algorithm
AESIG 2025, 1(1), 4-23;
https://doi.org/10.58244/aesig.253462
Review
A Review of Integrated CCUS-EOR and Storage Prediction and Optimization: From Accelerated Compositional Simulation to Data–Physics Coupled Intelligent Models
Abstract: Carbon dioxide enhanced oil recovery with geological storage has attracted increasing attention because it can simultaneously improve hydrocarbon recovery and reduce emissions. Accurate and efficient prediction of development performance, together with reliable support for injection–production design and optimization, has therefore become a central scientific and engineering
Keywords: CCUS-EOR; Numerical simulation; Reduced-order models; Deep learning; Data–physics coupling; Physics-informed neural networks.
AESIG 2025, 1(1), 24-53;
https://doi.org/10.58244/aesig.253464
Review
Review of Reservoir Closed-Loop Optimization Control Methods
Abstract: This paper presents a review of reservoir closed-loop optimization management technology, with a particular focus on its core software component—reservoir closed-loop optimization control technology. This technology constitutes a closed-loop process encompassing two fundamental steps: automatic history matching and production optimization. History matching
Keywords: Meshless method; Reservoir numerical simulation; Generalized finite difference method; Fluid-solid coupling; Numerical algorithm
AESIG 2025, 1(1), 54-65;
https://doi.org/10.58244/aesig.253465
Review
A Review of Gas Injection and Energy Supplement for Shale Condensate Gas Reservoirs
Abstract: Natural gas has become a core component of the global clean energy transition, and shale condensate gas reservoirs represent a high-value unconventional resource critical to improving national energy security, especially for countries with a coal-dominated energy mix like China. This paper reviews the current status of gas injection and energy supplement technologies
Keywords: Shale condensate gas reservoir; Component gradient; Nano-confinement effect; Projection-based embedded discrete fracture model (pEDFM); Gas injection and energy supplement
AESIG 2025, 1(1), 66-79;
https://doi.org/10.58244/aesig.253466
Article
Boundary-integral Type Neural Network (BINN) for Flow Problems in Reservoirs
Abstract: This study pioneers the application of Boundary-Integral Neural Networks (BINNs) to subsurface flow simulation, addressing steady-state single-phase flow governed by Laplace-type equations in hydrocarbon reservoirs. BINNs synergistically integrate boundary integral equations (BIEs) with deep learning to overcome limitations of traditional mesh-based methods
Keywords: Deep Learning; Boundary integral equations (BIEs); Physics-informed neural networks (PINNs); Boundary-Integral Neural Networks(BINNs); Meshless Reservoir Simulation; Dimensionality Reduction
AESIG 2025, 1(1), 80-94;
https://doi.org/10.58244/aesig.253463
