Search for Articles:
Journal:
Subject:
MOSP / Journals / Advanced Energy Systems and Intelligent Geoscience

Journal Menu

Home
Aims & Scope
Editorial Board
Reviewer Board
For Authors
Editorial & Publishing Policies
Article Processing Charge
Indexing & Archiving
Special Issues
Book Series
Meetings & Events
History & Awards
Contact Editor

Journal Browser

Vol.2 (2026) Iss. 1 / 2
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: Prof. Hui Zhao, Prof. Yuhui Zhou, Prof. 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
5 Articles | Volume 2 (2026)
Information & Contents - 2026.2(2)
AESIG   2026, 2(2), 0-2; 
Full text
Views：38
Download：1
Review
Early Access
The Evolution of AI in the Oil and Gas Industry From Digitization to Intelligent Decision-Making
Authors: Jiaxu Mei, Xiang Rao*
Abstract: Artificial intelligence and deep learning are becoming increasingly important in oil and gas field development, gradually becoming the development trend and research hotspot in the petroleum industry. Currently, over 70% of large oil and gas enterprises worldwide have listed the “physical-data dual-driven” model as their core strategy to make up for the shortcomings of [...] Read More.
Keywords: Artificial Intelligence, Deep Learning, Oil and Gas Development, Data-driven Model
AESIG   2026, 2(2), 4-16; 
Full text
Views：60
Download：0
Article
Early Access
An Intelligent Water Injection Decision-Making and Production Optimization Method Based on SAC Deep Reinforcement Learning
Authors: Tianyu Yang, Xiang Rao*, Shuhui Xu
Abstract: To address the failure of static water injection strategies in late-stage oilfield development caused by strong reservoir heterogeneity, this paper proposes an intelligent water injection and dynamic production optimization method using Soft Actor-Critic (SAC) deep reinforcement learning. By formulating waterflooding optimization as a Markov Decision Process (MDP), a Finite [...] Read More.
Keywords: Deep reinforcement learning; Soft Actor-Critic (SAC); Intelligent water injection decision-making; Closed-loop reservoir management; Spatial heterogeneity; Production optimization
AESIG   2026, 2(2), 17-33; 
Full text
Views：88
Download：0
Article
Early Access
Application of a Geologically Constrained Bayesian Prototypical Network for Few-Shot Lithofacies Identification in Lacustrine Carbonate Rocks
Authors: Xiang Cheng, Yukun Liu*, Mei Yang, Yiming Zhu, Yulin Du, Xiaolong Wang
Abstract: Accurate identification of lacustrine carbonate lithofacies is of critical importance for reservoir evaluation. However, conventional deep learning methods face significant bottlenecks in such complex settings, constrained by extreme class imbalance, the absence of uncertainty quantification in deterministic models, and insufficient geological prior knowledge. To address these [...] Read More.
Keywords: Lacustrine carbonate rock; Lithofacies identification; Bayesian prototypical network; Geological constraint; Uncertainty quantification; Few-shot learning
AESIG   2026, 2(2), 34-50; 
Full text
Views：115
Download：3
Article
Early Access
Prediction of Mineral Content and Petrophysical Parameters in Lacustrine Fine-Grained Mixed Sedimentary Rocks Based on a Physics-Informed Hybrid Deep Learning Framework
Authors: Yiming Zhu, Yukun Liu*, Shuya Chen, Yulin Du , Xiang Cheng, Xiaolong Wang
Abstract: To address the challenges of severe logging response overlap and quantitative mineral prediction caused by the strong heterogeneity of lacustrine carbonate reservoirs, this study proposes a hybrid framework integrating a physics-informed Gradient Boosting Decision [...] Read More.
Keywords: Lacustrine carbonate;Mineral content prediction;Physics-informed constraints; Temporal Convolutional Network (TCN);Well logging interpretation;Model interpretability
AESIG   2026, 2(2), 51-64; 
Full text
Views：88
Download：3
Article
Early Access
Zero-Shot Filling of FMI Blank Stripes Based on Deep Image Prior and an Attention-Enhanced Unet
Authors: Yulin Du, Yukun Liu*, Xiaolong Wang, Yiming Zhu, Xiang Cheng, Sile Wei
Abstract: Fullbore Formation MicroImager (FMI) logging provides high-resolution electrical images of the borehole wall and has been widely used for fracture interpretation, dip analysis, and complex reservoir evaluation. However, due to limited pad coverage, borehole diameter variations, and unstable tool–wall contact, original FMI images commonly contain longitudinal blank stripes [...] Read More.
Keywords: FMI logging; Blank-stripe filling; Deep image prior; U-Net; Poisson blending; Zero-shot learning
AESIG   2026, 2(2), 65-82; 
Full text
Views：99
Download：5
Submit Your Manuscript Now
Branches
HanLin Group
澳科中文官网
Book Center
Patent Center
Services
For Authors
Book Builder
Journal Builder
- Programs -
News & Highlights
Annual Best Books
Publication Support Program
About
About MOSP
Milestones
Partnership
Careers
Contact us
© 2021-2026 Macao Scientific Publishers (MOSP). All Rights Reserved.
www.mospbs.com | moaj.mospbs.com
0.658144s