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Energies
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Enhanced Oil Recovery: Numerical Simulation and Deep Machine Learning
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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 3731

Special Issue Editors

Dr. Maojie Chai

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Guest Editor
Department of Chemical & Petroleum Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada
Interests: unconventional resources; thermal recovery of heavy oil; underground H2 storage; CO2 storage; numerical simulation
Dr. Min Yang

E-Mail Website
Guest Editor
College of Safety and Environmental Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Interests: unconventional resources; thermal recovery of heavy oil; underground coal gasification; CO2 storage; numerical simulation
Special Issues, Collections and Topics in MDPI journals
Dr. Jinze Xu

E-Mail Website
Guest Editor
Department of Chemical and Petroleum Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
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.

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)
  • numerical simulation
  • machine learning
  • data-driven approach

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Published Papers (5 papers)

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Research

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25 pages, 1292 KiB  
Article
Screening Decommissioned Oil and Gas Pipeline Cleaners Using Big Data Analytics Methods
by Rongguang Li, Junqi Zhao, Ling Sun, Long Jin, Sixun Chen and Lihui Zheng
Energies 2025, 18(13), 3496; https://doi.org/10.3390/en18133496 - 2 Jul 2025
Viewed by 203
Abstract
Traditional methods, such as full-factorial, orthogonal, and empirical experiments, show limited accuracy and efficiency in selecting cleaning agents for decommissioned oil and gas pipelines. They also lack the ability to quantitatively analyze the impact of multiple variables. This study proposes a data-driven optimization [...] Read more.
Traditional methods, such as full-factorial, orthogonal, and empirical experiments, show limited accuracy and efficiency in selecting cleaning agents for decommissioned oil and gas pipelines. They also lack the ability to quantitatively analyze the impact of multiple variables. This study proposes a data-driven optimization approach to address these limitations. Residue samples from six regions, including Dalian and Shenyang, were analyzed for inorganic components using XRD and for organic components using GC. Citric acid was used as a model cleaning agent, and cleaning efficiency was tested under varying temperature, agitation, and contact time. Key variables showed significant correlations with cleaning performance. To further quantify the combined effects of multiple factors, multivariate regression methods such as multiple linear regression and ridge regression were employed to establish predictive models. A weighted evaluation approach was used to identify the optimal model, and a method for inverse prediction was proposed. This study shows that, compared with traditional methods, the data-driven approach improves accuracy by 3.67% and efficiency by 82.5%. By efficiently integrating and analyzing multidimensional data, this method not only enables rapid identification of optimal formulations but also uncovers the underlying relationships and combined effects among variables. It offers a novel strategy for the efficient selection and optimization of cleaning agents for decommissioned oil and gas pipelines, as well as broader chemical systems. Full article
(This article belongs to the Special Issue Enhanced Oil Recovery: Numerical Simulation and Deep Machine Learning)
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24 pages, 11727 KiB  
Article
Experimental Evaluation of Residual Oil Saturation in Solvent-Assisted SAGD Using Single-Component Solvents
by Fernando Rengifo Barbosa, Amin Kordestany and Brij Maini
Energies 2025, 18(13), 3362; https://doi.org/10.3390/en18133362 - 26 Jun 2025
Viewed by 283
Abstract
The massive heavy oil reserves in the Athabasca region of northern Alberta depend on steam-assisted gravity drainage (SAGD) for their economic exploitation. Even though SAGD has been successful in highly viscous oil recovery, it is still a costly technology because of the large [...] Read more.
The massive heavy oil reserves in the Athabasca region of northern Alberta depend on steam-assisted gravity drainage (SAGD) for their economic exploitation. Even though SAGD has been successful in highly viscous oil recovery, it is still a costly technology because of the large energy input requirement. Large water and natural gas quantities needed for steam generation imply sizable greenhouse gas (GHG) emissions and extensive post-production water treatment. Several methods to make SAGD more energy-efficient and environmentally sustainable have been attempted. Their main goal is to reduce steam consumption whilst maintaining favourable oil production rates and ultimate oil recovery. Oil saturation within the steam chamber plays a critical role in determining both the economic viability and resource efficiency of SAGD operations. However, accurately quantifying the residual oil saturation left behind by SAGD remains a challenge. In this experimental research, sand pack Expanding Solvent SAGD (ES-SAGD) coinjection experiments are reported in which Pentane -C5H12, and Hexane -C6H14 were utilised as an additive to steam to produce Long Lake bitumen. Each solvent is assessed at three different constant concentrations through time using experiments simulating SAGD to quantify their impact. The benefits of single-component solvent coinjection gradually diminish as the SAGD process approaches its later stages. ES-SAGD pentane coinjection offers a smaller improvement in recovery factor (RF) (4% approx.) compared to hexane (8% approx.). Between these two single-component solvents, 15 vol% hexane offered the fastest recovery. The obtained data in this research provided compelling evidence that the coinjection of solvent under carefully controlled operating conditions, reduced overall steam requirement, energy consumption, and residual oil saturation allowing proper adjustment of oil and water relative permeability curve endpoints for field pilot reservoir simulations. Full article
(This article belongs to the Special Issue Enhanced Oil Recovery: Numerical Simulation and Deep Machine Learning)
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15 pages, 5818 KiB  
Article
Nano-Water-Alternating-Gas Simulation Study Considering Rock–Fluid Interaction in Heterogeneous Carbonate Reservoirs
by Seungmo Ko, Hyeri Park and Hochang Jang
Energies 2024, 17(19), 4846; https://doi.org/10.3390/en17194846 - 27 Sep 2024
Cited by 1 | Viewed by 1085
Abstract
In carbonate reservoirs, nanoparticles can adhere to rock surfaces, potentially altering the rock wettability and modifying the absolute permeability. In the water-alternating-gas (WAG) process, the introduction of nanoparticles into the water phase, termed nano-water-alternating gas (NWAG), is a promising approach for enhancing oil [...] Read more.
In carbonate reservoirs, nanoparticles can adhere to rock surfaces, potentially altering the rock wettability and modifying the absolute permeability. In the water-alternating-gas (WAG) process, the introduction of nanoparticles into the water phase, termed nano-water-alternating gas (NWAG), is a promising approach for enhancing oil recovery and CO2 storage. The NWAG process can alter rock wettability and absolute permeability through the adsorption of nanoparticles on the rock surface. This study investigated the efficiency of the NWAG method, which utilizes nanofluids in CO2-enhanced oil recovery (EOR) processes to simultaneously recover oil and store CO2 using 1D core and 3D heterogeneous reservoir models. The simulation results of the 1D core model showed that applying the NWAG method enhanced both oil recovery and CO2 storage efficiency by increasing to 3%. In a 3D reservoir model, a Dykstra–Parsons coefficient of 0.4 was selected to represent reservoir heterogeneity. Additionally, the capillary trapping of CO2 during WAG injection was computed using Larsen and Skauge’s three-phase relative permeability hysteresis model. A sensitivity analysis was performed using the NWAG ratio, slug size, injection period, injection cycle, and nanofluid concentration. The results confirmed an increase of 0.8% in oil recovery and 15.2% in CO2 storage compared with the conventional WAG process. This mechanism suggests that nanofluids can enhance oil recovery and expand CO2 storage, improving the efficiency of both the oil production rate and CO2 storage compared to conventional WAG methods. Full article
(This article belongs to the Special Issue Enhanced Oil Recovery: Numerical Simulation and Deep Machine Learning)
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Review

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28 pages, 1181 KiB  
Review
Shear Wave Velocity in Geoscience: Applications, Energy-Efficient Estimation Methods, and Challenges
by Mitra Khalilidermani, Dariusz Knez and Mohammad Ahmad Mahmoudi Zamani
Energies 2025, 18(13), 3310; https://doi.org/10.3390/en18133310 - 24 Jun 2025
Viewed by 337
Abstract
Shear wave velocity (Vs) is a key geomechanical variable in subsurface exploration, essential for hydrocarbon reservoirs, geothermal reserves, aquifers, and emerging use cases, like carbon capture and storage (CCS), offshore geohazard assessment, and deep Earth exploration. Despite its broad significance, no [...] Read more.
Shear wave velocity (Vs) is a key geomechanical variable in subsurface exploration, essential for hydrocarbon reservoirs, geothermal reserves, aquifers, and emerging use cases, like carbon capture and storage (CCS), offshore geohazard assessment, and deep Earth exploration. Despite its broad significance, no comprehensive multidisciplinary review has evaluated the latest applications, estimation methods, and challenges in Vs prediction. This study provides a critical review of these aspects, focusing on energy-efficient prediction techniques, including geophysical surveys, remote sensing, and artificial intelligence (AI). AI-driven models, particularly machine learning (ML) and deep learning (DL), have demonstrated superior accuracy by capturing complex subsurface relationships and integrating diverse datasets. While AI offers automation and reduces reliance on extensive field data, challenges remain, including data availability, model interpretability, and generalization across geological settings. Findings indicate that integrating AI with geophysical and remote sensing methods has the potential to enhance Vs prediction, providing a cost-effective and sustainable alternative to conventional approaches. Additionally, key challenges in Vs estimation are identified, with recommendations for future research. This review offers valuable insights for geoscientists and engineers in petroleum engineering, mining, geophysics, geology, hydrogeology, and geotechnics. Full article
(This article belongs to the Special Issue Enhanced Oil Recovery: Numerical Simulation and Deep Machine Learning)
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25 pages, 7600 KiB  
Review
A Review of Enhanced Methods for Oil Recovery from Sediment Void Oil Storage in Underground Salt Caverns
by Xinxing Wei, Xilin Shi, Yinping Li, Peng Li, Mingnan Xu, Yashuai Huang and Yang Hong
Energies 2025, 18(2), 360; https://doi.org/10.3390/en18020360 - 16 Jan 2025
Cited by 7 | Viewed by 1088
Abstract
Salt caverns are recognized as an excellent medium for energy storage. However, due to the unique characteristics of China’s bedded salt formations, which contain numerous salt layers and a high concentration of insoluble impurities, significant accumulation at the bottom of salt caverns occurs, [...] Read more.
Salt caverns are recognized as an excellent medium for energy storage. However, due to the unique characteristics of China’s bedded salt formations, which contain numerous salt layers and a high concentration of insoluble impurities, significant accumulation at the bottom of salt caverns occurs, leading to the formation of extensive sediment voids. These sediment voids offer a potential space for underground oil storage, referred to as sediment void oil storage (SVOS). Oil recovery process from these sediment voids is a critical process. This paper summarizes the oil recovery technologies for SVOS and identifies four key factors—geological evaluation, stability evaluation, tightness evaluation, and oil storage capacity—all of which influence enhance oil recovery from sediment voids. This paper also outlines the overall oil recovery process, presents oil recovery experiments, and discusses oil recovery methods for enhancing oil recovery from sediment void. Additionally, it addresses the challenges of oil recovery in SVOS and explores its potential advantages and applications. The findings suggest that salt cavern sediment voids, as a promising storage space, provide a new approach to realize oil recovery and can overcome the limitations associated with cavern construction in high-impurity salt mines. The oil recovery from the sediment void is feasible, and China has rich rock salt and other convenient conditions to develop SVOS technology. Full article
(This article belongs to the Special Issue Enhanced Oil Recovery: Numerical Simulation and Deep Machine Learning)
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