Contribution of Artificial Intelligence/Big Data to Reservoir Engineering and Reservoir Modeling
A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".
Deadline for manuscript submissions: 20 February 2025 | Viewed by 401
Special Issue Editors
Interests: reservoir simulation; machine learning; seepage mechanics; underground energy storage
Interests: reservoir simulation; machine learning; seepage mechanics
Interests: unconventional oil and gas development; CCUS; numerical simulation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The ever-growing volume of data in the oil and gas industry presents a unique opportunity to explore the contribution of artificial intelligence (AI) and big data to reservoir engineering and reservoir modeling.
This Special Issue delves into how AI algorithms can leverage vast datasets for predictive analytics and real-time decision making, thus leading to enhanced reservoir characterization, the identification of optimal production zones, unprecedented production forecasting, and rapid model improvement through real-time data analysis.
We seek original research that optimizes reservoir production while fostering sustainable practices, focusing on novel AI techniques, big data integration in workflows, real-time optimization strategies, and the role of AI in balancing resource recovery with long-term reservoir health. This collaboration between data scientists, engineers, and geoscientists aims to pave the way for a new era of data-driven reservoir management.
Dr. Ming Yue
Dr. Shuhong Wu
Dr. Jianchun Xu
Guest Editors
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.
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Keywords
- artificial intelligence
- reservoir engineering
- reservoir modeling
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