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Vol.2 (2026) Iss. 1

Vol.1 (2025) Iss. 1

Advanced Energy Systems and Intelligent Geoscience

Advanced Energy Systems and Intelligent Geoscience (AESIG) is 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 development trends in multi-physics characterization, AI-driven energy modeling, and quantum computing applications. We welcome experts and scholars from academia and industry around the world to submit original research articles, reviews, and technical reports, contributing to the diversity and academic value of the journal.
The Advanced Energy Systems and Intelligent Geoscience is committed to promoting knowledge sharing and collaboration in the academic community. The journal will regularly publish important topics and research directions related to complex reservoir simulation, renewable energy systems, and carbon neutrality technologies, helping readers understand and grasp the latest scientific research achievements and industry trends. In addition, the journal will invite authoritative experts in the field to write special articles, comments, and case studies, providing readers with in-depth academic resources.
We hope to become a bridge between academia and industry through the Advanced Energy Systems and Intelligent Geoscience, promoting interdisciplinary cooperation and knowledge exchange worldwide, and contributing to the development of sustainable and intelligent energy systems. [Aims & Scope]

Publisher: Macao Scientific Publishers (MOSP)

Editor-in-Chief: Yuhui Zhou, Xiang Rao | [View the Editorial Board]

Statement: 2025 © MOSP. The journal complies with the Open Access License (CC BY 4.0)

Print ISSN: 3106-9886 | Online ISSN: 3106-9894

Indexing: Under review

Quick Links: Submission Guidelines | Submission System | Contact Editors

6 Articles | Volume 2 (2026)

Information & Contents - 2026.2(1)

AESIG 2026, 2(1), 1-3;

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Review

A Review of Numerical Simulation and Connectivity Analysis in Complex Media Reservoirs

Authors: Wentao Zhan*, Yixin Zhang, Liang Pu, Zhaolin Pu

Abstract: This paper reviews the key technologies and challenges in numerical reservoir simulation and inter-well connectivity analysis for complex media. Addressing the issues of complex fluid flow patterns and strong heterogeneity in deep-layer, deep-water, and unconventional reservoirs, this study systematically summarizes the advantages and disadvantages of three mainstream [...] Read More.

Keywords: Complex media reservoirs; Computational domain discretization; Numerical simulation; Inter-well connectivity

AESIG 2026, 2(1), 4-31;

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Review

Research Progress on CPU/GPU Heterogeneous Parallelism and Efficient Solvers in Large-Scale Complex Reservoir Simulation

Authors: Kang Wang, Xiang Rao*

Abstract: This paper aims to systematically review the latest evolutionary routes of High-Performance Computing (HPC) in the field of reservoir numerical simulation. First, starting from the underlying hardware architecture, the article details the fundamental differences in data transmission mechanisms, programming complexity, and acceleration performance between the CPU/GPU [...] Read More.

Keywords: Large-scale reservoir simulation; GPU acceleration; Heterogeneous parallelism; Linear solvers; Embedded Discrete Fracture Model (EDFM); Non-Neighbor Connections (NNC)

AESIG 2026, 2(1), 32-42;

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Article

A Numerical Study of Hydraulic Fracture Growth Under One Cyclic Injection

Authors: Seqiang Zhuo, Zaile Zhou*, Yinglun Qin, Yuzhu Kang, Hui Zhao, Lie Kong, Zhihong Kang

Abstract: In this paper, a more potential cyclic fracturing scheme, which alternates injection and flowback in each injection cycle, is proposed to promote the stimulation efficiency. Collection and Re-injection of all the flowback fluid into formation to create fractures in the following cycles achieves environmental sustainability. With 2D displacement discontinue method, we model [...] Read More.

Keywords: Cyclic injection; Flowback; Behavior of fracture; pressure amplitude; Fracture re-opening; Injection schedule

AESIG 2026, 2(1), 43-66;

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Article

Numerical Simulation and Sensitivity Analysis of Key Parameters for Modified Silicon-Based Nanoparticle Enhanced Oil Recovery in Low-Permeability Reservoirs

Authors: Chen Zhang, Xiang Rao*

Abstract: To solve the low recovery rate of low-permeability heterogeneous reservoirs in mid-late oilfield development, this study focuses on modified silicon-based nanoparticle EOR technology (strong migration, good environmental compatibility). A 3D two-phase two-component model was built via CMG-STARS (single-well homogeneous & single-injection four-production [...] Read More.

Keywords: Nanoparticle-enhanced oil recovery; Numerical simulation; CMG-STARS; Parameter sensitivity analysis; Heterogeneous reservoir; Reservoir simulation

AESIG 2026, 2(1), 67-82;

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Article

Study of Methane Hydrate Phase Transition and Multiphase Flow Characteristics: Effect of Pressure and Initial Hydrate Saturation

Authors: Huiru Sun*

Abstract: Hydrate reservoirs often experience phase transitions such as decomposition and reformation during production process, which can significant fluid flow and reduce gas recovery efficiency. To reveal the mechanism between hydrate phase transition and gas–water two-phase flow behaviors, a visualized experimental setup was developed to monitor hydrate phase [...] Read More.

Keywords: Methane hydrate resources; Phase transition characteristic; Multiphase flow; Hydrate saturation; Production pressure

AESIG 2026, 2(1), 83-90;

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Article

Study on High-Performance Prediction of Wellbore Multiphase Flow Pressure Drop Distribution Based on Artificial Neural Networks

Authors: Xi Ouyang, Xiang Rao*

Abstract: This paper constructs a proxy model to predict the pressure drop distribution in wellbore multiphase flow based on artificial neural networks (ANNs). 10,000 sets of high-quality samples involving 15 parameters involving wellbore multiphase flow and covering diverse working conditions were generated based on the Beggs-Brill algorithm, which ensures the physical [...] Read More.

Keywords: Wellbore multiphase flow; Pressure drop prediction; Neural network; Beggs-Brill algorithm; Coupled simulation; Machine learning

AESIG 2026, 2(1), 91-104;

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