Artificial Intelligence (AI) in Enhanced Oil Recovery
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".
Deadline for manuscript submissions: 20 February 2026 | Viewed by 1344
Special Issue Editors
Interests: hydraulic fracturing; reservoir simulation; optimization operations
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent years, the oil and gas industry has been undergoing a transformative shift towards incorporating cutting-edge technologies to optimize and improve various processes. One of the prominent areas benefiting from this technological revolution is enhanced oil recovery (EOR). EOR techniques play a crucial role in maximizing hydrocarbon extraction from reservoirs, thereby extending the productive life of oilfields. Among the emerging technologies, artificial intelligence (AI) has shown immense potential for revolutionizing EOR methodologies. This proposed Special Issue aims to explore the intersection of AI and enhanced oil recovery, shedding light on innovative research, methodologies, and case studies that highlight the integration of AI in enhancing oil recovery processes.
This Special Issue seeks to provide a comprehensive platform for researchers, engineers, and practitioners to present and discuss the latest advancements, challenges, and opportunities in the field of AI-driven enhanced oil recovery. The main objectives of this Special Issue include:
- AI-Driven Reservoir Characterization: Presenting AI-powered methodologies for accurate reservoir characterization, including modeling geological features, porosity, permeability, and fluid properties, to enable more precise reservoir simulation and performance prediction.
- Intelligent Reservoir Monitoring and Management: Showcasing AI applications in real-time reservoir monitoring, data assimilation, and decision-making. This could involve predictive maintenance, anomaly detection, and adaptive control to optimize production processes.
- Data-Driven EOR Technique Selection: Highlighting the utilization of AI algorithms for analyzing historical data and selecting the most suitable EOR techniques based on reservoir conditions that leads to improved recovery rates.
- Optimization of EOR Operations: Exploring AI-driven optimization strategies for designing injection strategies, well placement, and managing injection-production networks to maximize oil recovery while minimizing operational costs.
- Uncertainty and Risk Management: Addressing the incorporation of AI in handling uncertainties and risks associated with EOR projects, including probabilistic modeling, sensitivity analysis, and decision support systems.
- Case Studies and Practical Implementations: Presenting real-world case studies and success stories of AI integration in EOR projects, demonstrating the quantifiable impact of AI technologies on enhancing oil recovery.
Submission Topics:
Contributions to this Special Issue can include, but are not limited to, the following topics:
- Machine learning and deep learning applications in EOR;
- AI-based reservoir simulation and history matching;
- Predictive analytics for reservoir performance;
- Integration of IoT and AI in reservoir management;
- Genetic algorithms and optimization techniques for EOR design;
- AI-driven uncertainty quantification in reservoir modeling;
- Big data analytics for EOR decision-making;
- AI-driven optimization of chemical and thermal EOR methods;
- Techno-economic analysis of AI-integrated EOR projects.
Prof. Dr. Yuliang Su
Dr. Guanglong Sheng
Guest Editors
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- enhanced oil recovery
- artificial intelligence
- reservoir simulation
- real-time monitoring and management
- data-driven
- optimization operations
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