Against the backdrop of escalating global energy demand projected to rise by 50% within the next decade, this comprehensive review aims to evaluate and analyze the transformative potential of nanomaterials in Enhanced Oil Recovery (EOR) techniques. The study specifically examines the synergistic interactions between various nanoparticles (NPs) and chemical modifiers, while addressing the critical challenges in practical implementation. The scope encompasses a systematic assessment of different NPs types, their mechanisms of action, their optimization for field-scale applications, and the identification of research gaps that need to be addressed for industrial-scale deployment. The review methodology employs a multi-faceted analytical approach, combining systematic analysis of experimental studies with a critical evaluation of field implementation challenges. The performance of metal oxides and silica NPs was critically reviewed by examining their interactions with polymers, surfactants, and low salinity brines (LowSal) across diverse reservoir conditions. The analysis particularly focused on correlating NPs characteristics (type, concentration, size, and surface area) with their impact on chemical modifier adsorption, recovery factors, wettability alterations, and interfacial tension (IFT) modifications. Our analysis revealed that silica (SiO₂) and alumina (Al₂O₃) NPs demonstrate superior performance in EOR applications, with distinct advantages under specific reservoir conditions. SiO₂ NPs achieve 30–50% IFT reduction under optimal conditions, particularly at lower temperatures, while exhibiting excellent compatibility with existing EOR infrastructure. Al₂O₃ NPs exhibit remarkable stability across various temperature ranges, while zirconium oxide (ZrO₂) NPs show up to 40% IFT reduction at elevated temperatures, offering versatility for different reservoir types. Optimal NP concentration was identified between 0.1–0.5 wt%, with higher concentrations potentially causing adverse effects such as pore blockage and particle aggregation. The combination of NPs with surfactants or polymers demonstrated synergistic effects, with SiO₂ NPs–surfactant combinations achieving an additional 10–20% IFT reduction compared to SiO₂ NPs alone. Surface modifications and optimized salinity conditions improved NPs stability by 20–30% in high-salinity environments, addressing a key challenge in field applications. Importantly, recent field trials underscore these laboratory findings by demonstrating a near 98% increase in cumulative oil production (from 15,502 to 30,682 bbl) in a Colombian heavy-oil reservoir following Ni–Pd–doped Al₂O₃ nanofluid injection. These operations also improved API gravity (from 11.6° to 13.4°) and significantly reduced viscosity (from 39,987 cP to 11,734 cP), economically, the steam requirements and associated costs reduced with nanofluid treatments in only one field trial up to 48% cost reduction attributed to lower steam usage and enhanced production. Highlighting the practical impact and longevity of NPs-based EOR technologies in real-field settings. This review thus provides a systematic framework for selecting and optimizing NPs-based EOR systems while identifying critical research needs in scaling up laboratory successes to commercial deployment. The findings emphasize the need for long-term stability studies, cost-effective synthesis methods, and environmental impact assessments to fully harness the potential of nanotechnology in the petroleum industry.

Enhancing oil recovery efficiency is vital in the energy industry. This study investigates magnesium oxide (MgO) nanoparticles combined with sodium dodecyl sulfate (SDS) surfactants to reduce interfacial tension (IFT) and improve oil recovery. Pendant drop method measurements revealed a 70% IFT reduction, significantly improving nanoparticle dispersion stability due to SDS. Alterations in Zeta Potential and viscosity, indicating enhanced colloidal stability under reservoir conditions, were key findings. These results suggest that the MgO-SDS system offers a promising and sustainable alternative to conventional methods, although challenges such as scaling up and managing nanoparticle–surfactant dynamics remain. The preparation of MgO nanofluids involved magnetic stirring and ultrasonic homogenization to ensure thorough mixing. Characterization techniques included density, viscosity, pH, Zeta Potential, electric conductivity, and electrophoretic mobility assessments for the nanofluid and surfactant–nanofluid systems. Paraffin oil was used as the oil phase, with MgO nanoparticle concentrations ranging from 0.01 to 0.5 wt% and a constant SDS concentration of 0.5 wt%. IFT reduction was significant, from 47.9 to 26.9 mN/m with 0.1 wt% MgO nanofluid. Even 0.01 wt% MgO nanoparticles reduced the IFT to 41.8 mN/m. Combining MgO nanoparticles with SDS achieved up to 70% IFT reduction, enhancing oil mobility. Changes in Zeta Potential (from −2.54 to 3.45 mV) and pH (from 8.4 to 10.8) indicated improved MgO nanoparticle dispersion and stability, further boosting oil displacement efficiency under experimental conditions. The MgO-SDS system shows promise as a cleaner, cost-effective Enhanced Oil Recovery (EOR) method. However, challenges such as nanoparticle stability under diverse conditions, surfactant adsorption management, and scaling up require further research, emphasizing interdisciplinary approaches and rigorous field studies.

Wettability alteration through nano-assisted chemical Enhanced Oil Recovery (nano-cEOR) critically governs capillary pressure and multiphase flow in porous media. Contact angle (CA) quantifies wettability, yet experimental measurement remains costly, inconsistent, and operationally impractical, creating nanofluid screening bottlenecks. Existing machine learning (ML) models lack nanoparticle (NP)-specific descriptors essential for nano-cEOR physics. This study introduces a comprehensive ML framework predicting CA from 418 experimental data points, integrating NP-specific descriptors (type, concentration, size, functionalization), fluid properties, rock mineralogy, and reservoir conditions. Six algorithms were benchmarked across independent training, testing, and validation datasets with hyperparameter optimization and tenfold nested cross-validation. Multi-method sensitivity analysis integrating Sobol indices, SHAP values, and partial dependence plots identified operational thresholds and lithology-specific strategies. XGBoost Regressor (XGBR) achieved superior validation performance: R ² = 0.953, RMSE = 9.24°, MAE = 5.87°, and near-zero prediction bias (− 0.04°). Sensitivity analysis identified a minimum permeability of 0.1 mD for nano-cEOR viability, an optimal salinity window of 30,000–80,000 ppm, and synergistic NP-to-chemical ratios of 1:1 to 1.5:1. Rock-type analysis revealed lithology-dependent formulation inversion, with optimal NPs shifting between carbonates (ZrO ₂ , TiO ₂ ) and sandstones (Fe ₃ O ₄ , CuO). These findings advance nanofluid screening capability, support reservoir-specific formulation design, and provide decision-support guidelines for field-scale nano-cEOR deployment.

Accurate interfacial tension (IFT) prediction is crucial for optimizing enhanced oil recovery (EOR), as traditional measurement methods are time-consuming, costly, and often impractical under reservoir conditions. This study presents a novel machine learning (ML) framework for robust IFT prediction. A comprehensive dataset of 474 data points was curated from published literature, uniquely encompassing a wide variability in reservoir conditions and complex chemical formulations, including 10 distinct nanoparticle (NP) types, 4 surfactant classes, and various polymers. Multiple ML models were developed, including Artificial Neural Networks (ANN), Extreme Gradient Boosting (XGBoost), Random Forest (RF), and novel hybrid ensembles (ANN-RF and ANN-XGBoost). The results demonstrated that hybrid and deep learning approaches significantly outperformed simpler models. The hybrid ANN-RF model achieved superior validation accuracy (R² = 0.973, RMSE = 1.730), while the standalone ANN (Test R² = 0.942) and XGBoost (Test R² = 0.940) also showed strong generalization. A comprehensive sensitivity analysis identified chemical additive concentration as the dominant predictive factor, followed by NP concentration. Crucially, it revealed that salinity’s significant impact is driven primarily by high-order interaction effects. This work establishes a powerful, validated data-driven tool, highlighting the potential of ML to reduce experimentation and accelerate the design of optimized chemical EOR formulations. The novelty lies in combining hybrid models with comprehensive sensitivity analysis to deconstruct the complex, interaction-driven physics of NP-assisted EOR systems.