Enhanced Oil Recovery: Numerical Simulation and Deep Machine Learning
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "H: Geo-Energy".
Deadline for manuscript submissions: 10 November 2025 | Viewed by 2667
Special Issue Editors
Interests: unconventional resources; thermal recovery of heavy oil; underground H2 storage; CO2 storage; numerical simulation
Interests: unconventional resources; thermal recovery of heavy oil; underground coal gasification; CO2 storage; numerical simulation
Special Issues, Collections and Topics in MDPI journals
Interests: unconventional resources; thermal recovery of heavy oil; numerical simulation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Enhanced Oil Recovery (EOR) represents a critical aspect of petroleum engineering, as it aims to maximize the extraction of hydrocarbons from existing reservoirs. With the continuous growth in energy demand, efficient and effective EOR technologies are becoming increasingly crucial. Conventional methods, while effective, often face limitations in terms of their cost, environmental impact, and recovery efficiency. This is where the integration of numerical simulation and deep machine learning offers transformative potential.
Numerical simulation has long been a cornerstone in the planning and optimization of EOR processes. It allows for the detailed modelling of reservoir behaviours, the prediction of fluid flow, and assessment of various recovery techniques under different scenarios. However, the complexity and variability of geological formations often pose significant challenges to these simulations.
Deep machine learning, with its ability to handle large datasets and uncover intricate patterns, provides a powerful complement to numerical simulations. By leveraging advanced algorithms and computational power, machine learning can enhance predictive accuracy, optimize operational parameters, and even identify novel EOR strategies that were previously unattainable. The synergy between these two technologies promises a new era of innovation in EOR, driving both efficiency and sustainability.
This Special Issue aims to present and disseminate the latest advancements in the application of numerical simulation and deep machine learning to EOR. We invite researchers and practitioners to contribute findings, methodologies, and case studies that demonstrate the potential and challenges of integrating these technologies into EOR practices.
Topics of interest for publication include, but are not limited to, the following:
- Advanced numerical simulation techniques for EOR;
- Machine learning algorithms and their application in EOR;
- Hybrid methods combining numerical simulation and machine learning;
- Case studies of successful EOR implementations using these technologies;
- Optimization of EOR processes through simulation and machine learning;
- Predictive modelling of reservoir behaviour using deep learning;
- Data-driven approaches to enhance recovery efficiency;
- Integration of real-time data with simulation models;
- Environmental impact assessment using advanced modelling techniques;
- Future trends and challenges in EOR technology.
We encourage potential authors to submit their original research, review articles, and case studies that explore these cutting-edge approaches. By sharing your work, you will contribute to a collective endeavour to push the boundaries of what is possible in enhanced oil recovery, ensuring a more efficient and sustainable future.
Dr. Maojie Chai
Dr. Min Yang
Dr. Jinze Xu
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
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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 2600 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 (EOR)
- numerical simulation
- machine learning
- data-driven approach
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