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School of Petroleum Engineering, China University of Petroleum (East China), Qingdao, China
Department of Petroleum Engineering, College of Engineering and Mine, University of Alaska Fairbanks, Fairbanks, AK, USA
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Advances and Application in Intelligent Oil and Gas Field Development Technology

Abstract submission deadline
31 March 2026
Manuscript submission deadline
31 May 2026
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Topic Information

Dear Colleagues,

Intelligent oil and gas field development refers to the application of advanced technologies (such as Artificial Intelligence, Machine Learning, Big Data Analysis, Digital Twin, Large Model Technology, etc.) to optimize the exploration, production and maintenance of oil and gas fields. These technologies will enable the real-time monitoring and management of actual operations. They will facilitate reservoir characterization, increase production rates, and reduce operational costs. Intelligent oil and gas field development involves the integration of multiple disciplines (such as geology, carbon utilization and sequestration, critical minerals, petroleum engineering, data science, etc.) to form a comprehensive approach to oil and gas resources management.

This Topic aims to highlight the latest advances in intelligent oil and gas development and production, including cutting-edge technologies for real-time monitoring, production optimization, and predictive maintenance. The research topics covered in this Topic include, but are not limited to:

1. Artificial Intelligence and Machine Learning applications in the conventional and unconventional reservoirs.

2. Big Data Analysis techniques for decision-making in oil and gas resource development and production.

3. Digital Twin techniques for real-time monitoring and control during oil and gas well production.

4. Intelligent safety monitoring in oil and gas resources development.

5. Case studies and field trials of intelligent oil and gas field development.

6. Intelligent technology deployment and application of CO2 utilization and sequestration in the formation.

7. The application of large model technology about knowledge base construction and the use of a knowledge question-answering model-trained intelligent computing agent in the petroleum industry.

8. Any other Topics related to intelligent techniques during oil and gas resource development and production.

Prof. Dr. Liming Zhang
Dr. Yin Zhang
Topic Editors

Keywords

  • artificial intelligence
  • machine learning
  • big data analysis
  • digital twin
  • large model technology
  • geology
  • carbon utilization and sequestration
  • critical mineral
  • petroleum engineering
  • data science

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
AI
ai 		3.1 7.2 2020 18.9 Days CHF 1600 Submit
Applied Sciences
applsci 		2.5 5.3 2011 18.4 Days CHF 2400 Submit
Computers
computers 		2.6 5.4 2012 15.5 Days CHF 1800 Submit
Energies
energies 		3.0 6.2 2008 16.8 Days CHF 2600 Submit
Mathematics
mathematics 		2.3 4.0 2013 18.3 Days CHF 2600 Submit
Processes
processes 		2.8 5.1 2013 14.9 Days CHF 2400 Submit

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Published Papers (3 papers)

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27 pages, 3088 KiB  
Article
An Exploratory Study on Workover Scenario Understanding Using Prompt-Enhanced Vision-Language Models
by Xingyu Liu, Liming Zhang, Zewen Song, Ruijia Zhang, Jialin Wang, Chenyang Wang and Wenhao Liang
Mathematics 2025, 13(10), 1622; https://doi.org/10.3390/math13101622 - 15 May 2025
Viewed by 174
Abstract
As oil and gas exploration has deepened, the complexity and risk of well repair operations has increased, and the traditional description methods based on text and charts have limitations in accuracy and efficiency. Therefore, this study proposes a well repair scene description method [...] Read more.
As oil and gas exploration has deepened, the complexity and risk of well repair operations has increased, and the traditional description methods based on text and charts have limitations in accuracy and efficiency. Therefore, this study proposes a well repair scene description method based on visual language technology and a cross-modal coupling prompt enhancement mechanism. The research first analyzes the characteristics of well repair scenes and clarifies the key information requirements. Then, a set of prompt-enhanced visual language models is designed, which can automatically extract key information from well site images and generate structured natural language descriptions. Experiments show that this method significantly improves the accuracy of target recognition (from 0.7068 to 0.8002) and the quality of text generation (the perplexity drops from 3414.88 to 74.96). Moreover, this method is universal and scalable, and it can be applied to similar complex scene description tasks, providing new ideas for the application of well repair operations and visual language technology in the industrial field. In the future, the model performance will be further optimized, and application scenarios will be expanded to contribute to the development of oil and gas exploration. Full article
(This article belongs to the Topic Advances and Application in Intelligent Oil and Gas Field Development Technology)
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16 pages, 2816 KiB  
Review
Artificial General Intelligence (AGI) Applications and Prospect in Oil and Gas Reservoir Development
by Jiulong Wang, Xiaotian Luo, Xuhui Zhang and Shuyi Du
Processes 2025, 13(5), 1413; https://doi.org/10.3390/pr13051413 - 6 May 2025
Viewed by 378
Abstract
The cornerstone of the global economy, oil and gas reservoir development, faces numerous challenges such as resource depletion, operational inefficiencies, safety concerns, and environmental impacts. In recent years, the integration of artificial intelligence (AI), particularly artificial general intelligence (AGI), has gained significant attention [...] Read more.
The cornerstone of the global economy, oil and gas reservoir development, faces numerous challenges such as resource depletion, operational inefficiencies, safety concerns, and environmental impacts. In recent years, the integration of artificial intelligence (AI), particularly artificial general intelligence (AGI), has gained significant attention for its potential to address these challenges. This review explores the current state of AGI applications in the oil and gas sector, focusing on key areas such as data analysis, optimized decision and knowledge management, etc. AGIs, leveraging vast datasets and advanced retrieval-augmented generation (RAG) capabilities, have demonstrated remarkable success in automating data-driven decision-making processes, enhancing predictive analytics, and optimizing operational workflows. In exploration, AGIs assist in interpreting seismic data and geophysical surveys, providing insights into subsurface reservoirs with higher accuracy. During production, AGIs enable real-time analysis of operational data, predicting equipment failures, optimizing drilling parameters, and increasing production efficiency. Despite the promising applications, several challenges remain, including data quality, model interpretability, and the need for high-performance computing resources. This paper also discusses the future prospects of AGI in oil and gas reservoir development, highlighting the potential for multi-modal AI systems, which combine textual, numerical, and visual data to further enhance decision-making processes. In conclusion, AGIs have the potential to revolutionize oil and gas reservoir development by driving automation, enhancing operational efficiency, and improving safety. However, overcoming existing technical and organizational challenges will be essential for realizing the full potential of AI in this sector. Full article
(This article belongs to the Topic Advances and Application in Intelligent Oil and Gas Field Development Technology)
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13 pages, 844 KiB  
Article
Interwell Connectivity Analysis Method Based on Injection–Production Data Time and Space Scale Coupling
by Hong Ye, Jibin Deng, Jianjie Ma, Kai Zhang, Yujia Li, Huaqing Zhang and Kang Zhong
Processes 2025, 13(2), 373; https://doi.org/10.3390/pr13020373 - 29 Jan 2025
Viewed by 745
Abstract
In this paper, aiming at the challenges of injection–production optimization, especially the contradiction between injection and production in water flooding development of oil and gas fields in China, an interwell connectivity analysis method (TAGNN) based on the time–space scale coupling of injection–production data [...] Read more.
In this paper, aiming at the challenges of injection–production optimization, especially the contradiction between injection and production in water flooding development of oil and gas fields in China, an interwell connectivity analysis method (TAGNN) based on the time–space scale coupling of injection–production data is proposed. This method uses the existing injection–production well data, combined with the reservoir system seepage mechanics law, to quantitatively characterize and evaluate the interwell connectivity, which overcomes the limitations of traditional methods. The TAGNN method introduces asymmetric time alignment and advanced feature extraction technology to solve the problem of asymmetric injection–production data in time dimension, and considers the spatio-temporal scale coupling characteristics of injection–production data, which can capture the temporal variation and spatial distribution characteristics of data at the same time. The experimental results showed that this method more accurately reflected the interwell connectivity status and improved the fitting and prediction accuracy, compared with the existing GNN method. This method can promote the effective injection of water from the injection well to the production well and optimize the injection production structure and development plan, thereby improving the recovery rate. Full article
(This article belongs to the Topic Advances and Application in Intelligent Oil and Gas Field Development Technology)
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