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Advanced Techniques in Enhanced Oil Recovery (EOR) and Smart Reservoir Management

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "H1: Petroleum Engineering".

Deadline for manuscript submissions: 10 November 2026 | Viewed by 1553

Special Issue Editor

Dr. Tarek Arabi Ganat

E-Mail Website
Guest Editor
Department of Petroleum and Chemical Engineering, Sultan Qaboos University, Muscat 123, Oman
Interests: petroleum exploration technologies; drilling & completion strategies; reservoir management; sustainable practices (including carbon capture); risk management; digitalization in petroleum operations; economic analysis & market trends; greenhouse gas reduction; renewable energy integration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will focus on cutting-edge research and innovative applications in Enhanced Oil Recovery (EOR) and modern reservoir management. We seek contributions that explore novel chemical EOR methods, such as the use of polymeric nanofluids and surfactants, as well as advanced computational approaches, including Machine Learning and AI, for reservoir characterization and production optimization. This Issue will also cover topics related to conformance control, water cut reduction, and the mitigation of formation damage, aiming to bridge the gap between fundamental research and field-scale implementation for sustainable hydrocarbon recovery. This collection of papers will serve as a vital resource for researchers and industry professionals seeking to advance the state of the art in reservoir engineering. The scope includes both theoretical and experimental studies, as well as case studies demonstrating successful field applications. We encourage submissions that address the economic and environmental aspects of these technologies, ensuring a holistic view of future energy production. This Special Issue is a critical platform for sharing knowledge that will drive the next generation of smart and efficient oil recovery processes.

Dr. Tarek Arabi Ganat
Guest Editor

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)
  • nanofluids
  • reservoir simulation
  • machine learning
  • water cut management
  • formation damage
  • production optimization
  • smart wells

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Related Special Issue

Published Papers (3 papers)

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Research

34 pages, 5204 KB  
Article
Enhancing Reservoir Simulation History Matching Using SHAP Value for Parameter Range Selection
by Grachik Eremyan, Adel M. Magdeev, Mohammed Al-Shargabi, Ivan V. Matveev, Ivan E. Smirnov, Gleb Y. Shishaev and Shadfar Davoodi
Energies 2026, 19(10), 2285; https://doi.org/10.3390/en19102285 - 9 May 2026
Viewed by 224
Abstract
Accurate reservoir history matching is indispensable for reliable subsurface forecasting and optimal field development; however, it remains a formidable challenge due to the high dimensionality of geological parameter spaces, strong nonlinear dynamics, and the competing demands of computational efficiency and data fidelity. Conventional [...] Read more.
Accurate reservoir history matching is indispensable for reliable subsurface forecasting and optimal field development; however, it remains a formidable challenge due to the high dimensionality of geological parameter spaces, strong nonlinear dynamics, and the competing demands of computational efficiency and data fidelity. Conventional automated history matching workflows frequently rely on Tornado sensitivity analysis, which evaluates parameters in isolation and fails to capture critical interdependencies that govern reservoir response, leading to inefficient exploration and suboptimal convergence. This study, therefore, introduces a novel Shapley Additive Explanations (SHAP)-assisted adaptive history matching framework that integrates SHAP with a CatBoost (v1.0.0) regressor to enable quantitative, model-agnostic assessment of parameter contributions while explicitly resolving non-linear feature interactions unattainable with conventional methods. The workflow establishes a systematic methodology for refining parameter uncertainty ranges prior to optimization through SHAP dependence plots, which identify subspaces where the objective function decreases, yielding 25–68% range reductions across all cases without compromising geological plausibility. Both SHAP-assisted and Tornado-based workflows, coupled with identical Particle Swarm Optimization settings, are rigorously validated on two synthetic reservoirs (SRM-6, Egg) and a real Siberian field case. Results demonstrate that the SHAP-assisted approach achieves equivalent or superior history match quality while reducing required optimization cycles by 6–60%, depending on model complexity, delivering lower objective function values and improved alignment with observed data. Computational efficiency is further enhanced by reusing the Latin Hypercube Sampling dataset for surrogate training, sensitivity analysis, and range refinement, contrasting sharply with Tornado’s requirement for 2N + 1 dedicated simulation runs with limited reusability. This framework advances interpretable, data-driven history matching by providing engineers with a systematic method to prioritize calibration efforts, reduce computational burden, and improve forecast reliability in complex reservoir systems. Full article
(This article belongs to the Special Issue Advanced Techniques in Enhanced Oil Recovery (EOR) and Smart Reservoir Management)
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15 pages, 5064 KB  
Article
Physics-Guided Machine Learning with Flowing Material Balance Integration: A Novel Approach for Reliable Production Forecasting and Well Performance Analytics
by Eghbal Motaei, Tarek Ganat and Hai T. Nguyen
Energies 2026, 19(9), 2022; https://doi.org/10.3390/en19092022 - 22 Apr 2026
Viewed by 483
Abstract
Reliable production forecasting is a critical task for evaluating asset valuation and commercial performance in oil and gas reservoirs. Conventional short-term forecasting methods, such as Arps’ decline curve analysis, rely on simple mathematical curve fitting and often oversimplify reservoir performance. On the other [...] Read more.
Reliable production forecasting is a critical task for evaluating asset valuation and commercial performance in oil and gas reservoirs. Conventional short-term forecasting methods, such as Arps’ decline curve analysis, rely on simple mathematical curve fitting and often oversimplify reservoir performance. On the other hand, long-term forecasting requires complex multidisciplinary models that integrate geophysics, reservoir engineering, and production engineering, but these approaches are time-consuming and have high turnaround times. To bridge the gap between long and short-term production forecasts, reduced-physics models such as Blasingame type curves have been developed, incorporating transient well behaviour derived from diffusivity equations and Darcy’s law. These models assume homogeneity and uniform reservoir properties, enabling faster results while honouring pressure performance. However, despite their efficiency, they still face limitations in reliability, particularly when extended to long-term forecasts. This paper proposes a hybrid modelling approach that integrates flowing material balance (FMB) concepts into physics-informed neural networks (PiNNs) and machine learning models to improve the accuracy and reliability of production forecasting. The proposed methodology introduces two hybrid strategies: physics-informed models enriched with FMB feature, and PiNNs. The first proposed hybrid model uses a created FMB-derived feature as input to neural networks. The second PiNN model embeds data-driven loss functions with a physics-based envelope to reflect reservoir response into the machine learning model. The primary loss function is mean squared error, ensuring minimization of data misfit between predicted and observed production rates. The study validates both proposed physically informed neural network models through performance metrics such as RMSE, MAE, MAPE, and R2. Results application on field data shows that the integration of FMB into neural network models using the PiNN concept guides the neural network models to predict the production rates with higher reliability over the full span of the tested data period, which was the last year of unseen production data. Additionally, the proposed PiNN model is able to predict the well productivity index via hyper-tuning of the PiNN model. Furthermore, the PiNN is not improving the metric performance of conventional neural networks, as it has to satisfy an additional material balance equation. This is due to a lower degree of freedom in the PiNN models. Full article
(This article belongs to the Special Issue Advanced Techniques in Enhanced Oil Recovery (EOR) and Smart Reservoir Management)
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27 pages, 6347 KB  
Article
Experimental Confirmation of Increasing Oil Displacement Efficiency Using a Slug of Aqueous Suspension of Surfactants and Nanoparticles Followed by Flooding
by Farit Safarov, Aleksander Voloshin, Aleksey Telin, Andrey Fetisov, Lyubov Lenchenkova, Vladimir Dokichev, Ravil Yakubov, Rida Gallyamova, Artem Ratner, Natalia Sergeeva, Ekaterina Gusarova, Artem Pavlik and Anatoly Politov
Energies 2026, 19(4), 1059; https://doi.org/10.3390/en19041059 - 18 Feb 2026
Viewed by 461
Abstract
To improve the efficiency of injecting intensifying chemical slugs into injection wells, new formulations have been proposed. These compositions are based on high-tonnage surfactants combined with industrially produced nanoparticles. Experiments show that adding silica- or carbon-based nanoparticles to surfactant compositions doubles the oil [...] Read more.
To improve the efficiency of injecting intensifying chemical slugs into injection wells, new formulations have been proposed. These compositions are based on high-tonnage surfactants combined with industrially produced nanoparticles. Experiments show that adding silica- or carbon-based nanoparticles to surfactant compositions doubles the oil displacement coefficient from Pashian sandstones. Carbon nanoparticles derived from shungite mineral were also tested. It was found that during the filtration of the surfactant solution, the increase in the oil displacement coefficient is always lower than during the filtration of the same solution in the presence of nanoparticles. This composition contains anionic and nonionic surfactants in a 1:2 ratio at a 1% concentration in fresh water, with a 1% nanoparticle additive. It increases the oil displacement coefficient by 19.0–23.2% after waterflooding. It has been established that in the proposed technology for near-wellbore formation treatment, the role of nanoparticles lies in a transport function due to the formation of nanoparticle aggregates with surfactant micelles, representing dynamic structures sized 25–75 μm. These aggregates break apart when passing through narrow pore throats. This delivers surfactants directly to the oil–rock interface, mobilizing residual oil and improving displacement. Nanoparticles of silica with different wettability, during filtration, are deposited in pore channels, leading to intra-pore flow redistribution. Together with the increased microscopic sweep efficiency from surfactants, it results in lower residual oil saturation. Full article
(This article belongs to the Special Issue Advanced Techniques in Enhanced Oil Recovery (EOR) and Smart Reservoir Management)
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