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

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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13 pages, 844 KiB  

Open AccessArticle

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

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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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