Enhancing Oil and Gas Recovery: Experimental Study, Numerical Simulation and Deep Machine Learning
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "H1: Petroleum Engineering".
Deadline for manuscript submissions: 10 June 2026 | Viewed by 26
Special Issue Editors
Interests: unconventional resources; thermal recovery of heavy oil; underground H2 storage; CO2 storage; numerical simulation
Interests: conformance control; EOR; nanomaterials; catalysts
Interests: chemical flooding enhanced oil recovery; emulsification behavior characterization and control; multiphase fluid microscale flow
Special Issues, Collections and Topics in MDPI journals
Interests: drilling fluid; nanomaterials; polymeric agent; wellbore stability; molecular simulation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Enhanced Oil/Gas Recovery (EOR/EGR) represents a critical aspect of petroleum engineering, as it aims to maximize the extraction of hydrocarbons from existing reservoirs. Due to the continuous growth in energy demand, the development of efficient and effective EOR technologies is 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 the 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.
Building upon the success of the first edition, the second edition of this Special Issue continues 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/EGR;
- Advanced experimental study techniques for EOR/EGR;
- Machine learning algorithms and their application in EOR/EGR;
- 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. Zhe Sun
Dr. Zheyu Liu
Dr. Bo Liao
Dr. Zuhao Kou
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 250 words) can be sent to the Editorial Office for assessment.
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. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.
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)
- enhanced gas recovery (EGR)
- experimental study
- numerical simulation
- machine learning
- data-driven approach
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.
