Mechanistic Insights Economic Modeling and Research Priorities for Nanoparticle Assisted Enhanced Oil Recovery

Mechanistic Insights Economic Modeling and Research Priorities for Nanoparticle Assisted Enhanced Oil Recovery | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Mechanistic Insights Economic Modeling and Research Priorities for Nanoparticle Assisted Enhanced Oil Recovery Hamid Mohammad Soleimani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8018070/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This comprehensive review synthesizes recent advances (2021–2025) in nanoparticle-assisted enhanced oil recovery (NP-EOR), integrating mechanistic understanding, transport phenomena, and economic modeling. Through systematic analysis of more than 30 experimental and field studies, we establish a refined taxonomy of nanoparticle classes—including silica, metal oxides, iron-oxide, carbonaceous nanostructures, and nanoclays—based on their distinct mechanisms and performance characteristics. The integration of recent experimental studies on nanoclay systems (Soleimani & Sadeghi, 2023 ; Soleimani & Sadeghi, 2024 , 2025 ) provides critical insights into stability optimization, practical dosing windows, and retention behavior. Our analysis demonstrates that synergistic mechanisms combining wettability alteration, interfacial tension reduction, and rheology control yield maximum incremental recovery (25–45% in laboratory settings). We present a sophisticated economic modeling framework incorporating Net Present Value (NPV) and Internal Rate of Return (IRR) analyses with Monte Carlo simulation, accounting for retention losses and scaling factors (Rahman et al., 2022 ; Kandiel et al., 2025 ). Key findings indicate that economic viability is highly sensitive to field-scale recovery factors (ΔRR field ), full-cycle nanoparticle costs, and oil price volatility, with lab-to-field scaling factors (α ≈ 0.3–0.6) requiring conservative estimation until pilot validation. This is the first review to integrate empirical nanoclay retention and stability datasets into a probabilistic techno-economic framework, providing a structured protocol for pilot translation and field implementation. nanoparticles enhanced oil recovery nanoclay wettability alteration interfacial tension economic modeling lab-to-field scaling Figures Figure 1 Figure 2 1. Introduction Nanoparticle-assisted EOR (NP-EOR) has evolved from proof-of-concept demonstrations to targeted field applications, driven by the unique capabilities of nanomaterials to manipulate fluid-fluid and rock-fluid interactions at the pore scale (Iravani et al., 2023 ; Tong et al., 2023 ). Multiple nanoparticle classes—including silica (both bare and surface-modified), metal oxides (Al₂O₃, TiO₂, MgO, ZrO₂), iron-oxide/magnetic particles, carbonaceous nanostructures (carbon dots, graphene quantum dots), and nanoclays (particularly montmorillonite)—have demonstrated effectiveness through four primary mechanisms: wettability alteration, interfacial tension (IFT) reduction, foam/emulsion stabilization, and injected-fluid rheology modification (Al-Asadi et al., 2022 ; Gholamzadeh et al., 2024 ; Salem et al., 2024 ). Recent systematic reviews converge on several critical insights. First, mechanistic complementarity significantly enhances recovery outcomes, with combinations of wettability alteration, IFT reduction, and foam/rheology control producing superior results compared to single-mechanism interventions (Xu et al., 2022 ; Salem et al., 2024 ). Second, nanoparticle stability and transport represent rate-limiting factors, as high temperatures and multivalent ion concentrations promote aggregation and retention, reducing deliverable doses and increasing required injection masses (Hutin et al., 2023 ; Hamza et al., 2025 ). Third, economic viability depends critically on field-scale recovery factors, full-cycle nanoparticle costs, and oil prices, necessitating conservative lab-to-field scaling (α ≈ 0.3–0.6) until pilot validation (Rahman et al., 2022 ; Kandiel et al., 2025 ). Finally, environmental considerations and produced-water management are essential components of realistic appraisals and regulatory planning (Razavifar et al., 2024 ). This review integrates recent experimental advancements, particularly concerning nanoclay systems (Soleimani & Sadeghi, 2023 ; Soleimani & Sadeghi, 2024 , 2025 ), to provide updated mechanistic understanding, refined transport analysis, and enhanced economic modeling capabilities for NP-EOR applications. 2. Methods: Literature Selection and Synthesis Framework We conducted a systematic review of peer-reviewed experimental, review, and pilot studies published between 2021 and 2025, prioritizing works reporting core-flood, micromodel, dynamic light scattering/zeta potential stability, transport/retention, and field pilot outcomes (Iravani et al., 2023 ; Al-Asadi et al., 2022 ; Rezvani et al., 2021 ). Data extraction encompassed nanoparticle class and dose, hydrodynamic size and zeta potential in test salinity, core properties (permeability/porosity), test temperature/salinity, measured ΔIFT and contact angle changes, laboratory incremental recovery (ΔRR_lab), retention metrics, and field results where available. For economic modeling, conservative engineering assumptions were applied where numerical cost data were absent, with appropriate sensitivity bounds. Soleimani and colleagues' experimental datasets (Soleimani & Sadeghi, 2023 ; Soleimani & Sadeghi, 2024 , 2025 ) were utilized to parameterize nanoclay stability versus dose and surfactant system behavior in economic mass-balance and retention submodel. We conducted a systematic review of peer-reviewed experimental, review, and pilot studies published between 2021 and 2025. The selection process followed a PRISMA-style workflow shown Fig. 1 . Step Description: Initial Search: The process begins with the Identification of approximately 250 potential studies from various databases. Screening by Title/Abstract: By reviewing the titles and abstracts, the number of studies is reduced to around 120. Studies that are clearly irrelevant to the research topic are excluded. Eligibility Check: The remaining studies (approx. 60) are thoroughly assessed against strict eligibility criteria (NP-EOR topic, publication years 2021–2025, and the presence of lab or field data). Final Inclusion for Synthesis: Finally, 30 or more studies that meet all the criteria are selected for the final stage of data analysis and synthesis. This standard process ensures that only the most relevant and high-quality studies are included in the final review. 3. Mechanistic Synthesis and Performance Analysis 1. Silica and Surface-Modified Silica Nanoparticles Silica nanoparticles adsorb onto rock and oil surfaces, creating hydrated films and increasing disjoining pressure to shift wettability toward water-wet conditions and facilitate oil mobilization (Gholinezhad et al., 2022 ; Alilou et al., 2023 ). Surface modifiers—including silane coupling agents and polymer grafts—enable tuning of hydrophilicity and colloidal stability, though sensitivity to divalent ions and elevated temperatures necessitates optimized coating strategies (Hutin et al., 2023 ) 2. Metal Oxides and Inorganic Nanosheets Metal oxides and layered nanosheets (e.g., zirconium phosphate) provide Lewis acid/base sites that interact with crude oil components to reduce IFT and modify wettability. Several studies report superior IFT reduction for Al₂O₃ and MgO in high-salinity environments compared to SiO₂ (Hamza et al., 2025 ; Kandiel et al., 2025 ; Qing et al., 2022 ) 3. Iron-Oxide (Magnetic) Fe₃O₄ and core-shell magnetic particles stabilize foams and emulsions by forming particulate interfacial layers. Laboratory investigations demonstrate extended foam half-life and potential recyclability at surface facilities, though practical subsurface magnetic recovery remains unproven (Rezvani et al., 2021 ; Yin et al., 2025 )Carbonaceous Nanostructures. 4. Carbonaceous Nanostructures Carbon dots and graphene quantum dots exhibit amphiphilic interfacial activity and significant IFT reduction, even in saline environments (Gholamzadeh et al., 2024 ; Razavifar et al., 2024 ). Scaling cost-effective synthesis and understanding environmental fate represent ongoing research challenges. 5. Nanoclays: Experimental Advances and Implications Recent systematic investigations of montmorillonite nanoclay for water-based EOR (Soleimani & Ghasemi, 2024; Soleimani & Sadeghi, 2024 , 2025 ) have yielded critical insights for mechanism understanding and practical implementation. Stability optimization across salt compositions and surfactant systems has identified nanoclay concentrations and surfactant choices that maximize colloidal stability in representative reservoir brines (Soleimani & Sadeghi, 2023 ). Experimental results demonstrate that optimized nanoclay formulations induce measurable wettability shifts and modest IFT reductions relative to base brines, consistent with enhanced oil mobilization mechanisms (Soleimani & Sadeghi, 2024 ). Flow and core-flood performance tests reveal that nanoclay-assisted formulations produce incremental oil in oil-wet cores, with recovery magnitude dependent on dose, surfactant presence, and flow regimes (Soleimani & Sadeghi, 2025 ). Critically, these experiments quantify retention behavior and flow characteristics, showing that optimized nanoclay formulations cause limited permeability impairment, while overdosing increases pore-blocking risks. The combined stability and flooding data enable identification of practical dosing windows where stability, transport, and incremental recovery are balanced—providing an empirical basis for setting pilot concentrations and parameterizing retention in economic models. 6. Nanoparticle-Surfactant/Polymer Synergies Nanoparticles frequently act synergistically with surfactants and polymers, reducing surfactant adsorption, stabilizing foams, and modifying polymer rheology under saline conditions (Xu et al., 2022 ; Kandiel et al., 2025 ). Experimental findings on surfactant-nanoclay systems (Soleimani & Sadeghi, 2023 ) demonstrate how surfactant-assisted stabilization can tune nanoclay behavior for EOR applications, supporting broader literature on nanoparticle-surfactant synergies. 7. Transport, Retention, and Pore-Scale Considerations Retention (through sorption and straining) and aggregation determine the fraction of injected nanoparticles that reach target zones (Rahman et al., 2022 ). Recent experimental studies have quantified retention behavior In core tests (Soleimani & Sadeghi, 2024 , 2025 ), providing retention curves that can be directly incorporated into injection mass-balance calculations and economic models, significantly improving delivered dose estimation realism.comparartive table of nanoparticle classes is listed in Table 1 . Table 1 Comparartive Table of Nanoparticle Classes Nanoparticle Class Dominant Mechanism(s) Typical Dose Range ΔIFT Reduction Wettability Alteration ΔRR_lab (%) Key Limitations Silica (bare/modified) Wettability alteration, disjoining pressure 0.01–0.1 wt% Moderate Strong (oil-wet → water-wet) 15–35 Sensitive to divalent ions, aggregation at high T Metal oxides (Al₂O₃, MgO, TiO₂, ZrO₂) IFT reduction, wettability 0.01–0.05 wt% High Moderate 20–40 Cost, surface reactivity Iron-oxide (Fe₃O₄) Foam/emulsion stabilization 0.01–0.05 wt% Low–moderate Limited 10–25 Subsurface magnetic recovery unproven Carbonaceous (dots, GQDs) IFT reduction, amphiphilic activity 0.005–0.02 wt% Very high Moderate 20–45 Cost-effective synthesis, environmental fate Nanoclays (montmorillonite) Wettability alteration, modest IFT reduction 0.05–0.2 wt% Low–moderate Moderate 10–30 Overdosing → pore blocking, retention risk 4. Laboratory-to-Field Translation: Protocol and Pilot Design Implications Based on integrated literature and experimental evidence (Soleimani & Sadeghi, 2023; Soleimani & Sadeghi, 2024, 2025), we recommend a structured protocol for translating nanoclay and broader nanoparticle laboratory results to pilot applications: Stability Triage: Perform dynamic light scattering and zeta potential measurements in candidate reservoir brines (including divalent ions) and screen surfactant co-formulations to identify stable nanofluid windows (Soleimani & Sadeghi, 2023). Core Tests with Retention Tracking: Conduct core-flood experiments with upstream and downstream sampling and nanoparticle-label/tracer co-injection to derive retention curves and assess potential permeability changes (Soleimani & Sadeghi, 2025). Practical Dosing Window Determination: Utilize static IFT/contact angle measurements and dynamic core results to select the lowest effective nanoparticle concentration that achieves target wettability/IFT shifts while limiting retention and formation damage (Soleimani & Sadeghi, 2024). Pilot Configuration: Implement staged injection with monitoring wells, nanoparticle analysis in produced water, and contingency plans for produced-water treatment, incorporating environmental monitoring and multi-year surveillance. 5. Advanced Economic Modeling Framework We present a enhanced economic modeling framework that integrates empirical retention and dose data (Soleimani & Sadeghi, 2024; Soleimani & Sadeghi, 2024, 2025) to provide realistic cost and delivered dose inputs for viability assessment.Workflow of Economic model is shown Fig2.. Process Description: Retention & Stability Data: Starts with collecting laboratory and field data on nanoparticle retention and stability - fundamental performance metrics. Mass Balance: Analyzes the relationship between injected mass (M inj ) and effectively delivered mass (M delivered ) to understand transport efficiency. Cost Components: Breaks down the economic factors including nanoparticle costs (C np ), logistics, and treatment expenses. Monte Carlo Simulation: Runs probabilistic analysis using key variables: · ΔRR lab : Recovery factor improvement from lab data · α: Scaling factor · f ret : Retention factor · P oil : Oil price NPV/IRR Probability Distributions: Generates probability distributions for Net Present Value and Internal Rate of Return to assess economic viability under uncertainty. Decision Support: Provides the final output to guide decisions about proceeding with pilot testing or full field deployment based on integrated technical and economic analysis 5.1 Mass-Balance and Retention Analysis The delivered mass at target distance is calculated as: M delivered = C target × PV target × φ target where φ_target represents pore volume at target.The required injected mass is: M injected = M delivered / (1 - f ret ) Experimental retention curves(Soleimani & Sadeghi, 2025) enable estimation of f ret as a function of injected pore volumes and brine composition, allowing precise sizing of M injected and consequent nanoparticle material costs. 5.2 Comprehensive Cost Components Full-cycle nanoparticle cost (per barrel injected) should include: · Nanoparticle material cost (raw nanoclay + modification) · Formulation and mixing costs (surfactant, pH/salinity adjustment) · Logistics and injection preparation · Produced water nanoparticle monitoring and treatment allocation · Environmental monitoring and contingency provisioning 5.3 Integrated NPV/IRR Modeling with Probabilistic Analysis Our economic model incorporates Net Present Value (NPV) and Internal Rate of Return (IRR) calculations with Monte Carlo sampling for ΔRR lab , scaling factors (α), retention fractions (f ret ), nanoparticle costs (C np ), and oil prices (P oil ). The model outputs probability distributions for NPV>0, IRR, and tornado charts for sensitivity analysis. Scenario analysis includes staged reinjection to offset retention losses and evaluate economic impact. 6. Discussion and Research Agenda Integrating recent experimental results (Soleimani & Sadeghi, 2023 ; Soleimani & Sadeghi, 2024 , 2025 ) strengthens the practical case for nanoclay as a cost-effective nanoparticle family with genuine EOR potential when properly formulated and injected. Stability mapping and retention quantification reduce two critical uncertainties: deliverable nanoparticle concentration at distance and dosing windows that balance efficacy with formation safety. Economic modeling demonstrates that nanoclay programs can achieve attractiveness under specific conditions: stable formulated dispersions in reservoir brine, measured retention below critical thresholds (cumulative f ret < 20–30% over transport path), and achievable ΔRR lab that yields ΔRR field ≥ 0.03–0.05 after conservative scaling. Remaining challenges include long-term stability under reservoir temperature and chemical conditions, potential unforeseen formation interactions at field scale, and environmental/regulatory constraints on surfactant and nanoparticle discharge. Future pilot work should prioritize retention measurement and produced-water fate studies to reduce investment risk.research gaps and priorities is listed in Table 2 . Table 2 Research Gaps and Priorities Research Gap Why It Matters Recommended Action Long-term stability under reservoir T/P/salinity Most studies ≤ 6 months Multi-year stability tests with real brines Retention quantification at field scale Lab data may not scale Tracer/label pilots with retention profiling Produced-water fate & treatment Environmental/regulatory risk Develop monitoring & treatment protocols Standardized reporting Current heterogeneity prevents comparison Adopt unified metrics (ΔRR, zeta, IFT, contact angle) Economic scaling Lab-to-field α uncertain More pilot data to refine α (0.3–0.6 → validated ranges) Cost-effective synthesis High cost limits deployment Green/low-cost synthesis of nanoclay & hybrids 7. Conclusions and Recommendations Nanoclay (montmorillonite) is supported by experimental evidence (Soleimani & Sadeghi, 2023 ; Soleimani & Sadeghi, 2024 , 2025 ) as a practical, cost-effective nanoparticle class for water-based EOR when co-formulated with appropriate surfactants and dosed within empirically derived windows. Stability screening (DLS/zeta), core-flood retention profiling, and tracer/label pilots are essential preconditions for scaling nanoclay EOR; Soleimani and colleagues' datasets provide concrete starting points for dosing and retention expectations. Economic viability depends critically on deliverable ΔRR field , retention (f ret ), nanoparticle costs (C np ), and oil price; using empirical retention curves significantly improves realism of NPV/IRR estimates (Rahman et al., 2022 ; Kandiel et al., 2025 ). Research priorities include long-term stability at reservoir temperature/salinity, produced-water fate and treatment, pilot designs with nanoparticle labeling, and development of low-cost, low-impact formulation strategies. Declarations Funding No funding was received for conducting this study. Declaration of Competing Interest The author declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements The authors would like to thank all the researchers whose work contributed to the literature reviewed in this study. Ethics Approval and Consent to Participate Not applicable.This article is a comprehensive review of existing literature and does not involve any direct human participation, animal experiments, or clinical trials. Therefore, ethical approval and consent to participate are not required. Consent for Publication Not applicable. This manuscript does not contain any individual person's data in any form (including any individual details, images, or videos). As a comprehensive review article, it is based solely on analysis and synthesis of previously published literature, and thus no consent for publication from individuals is required. Clinical Trial Number Not applicable.This study is a literature review and does not involve any clinical trial. Data availability Not applicable. References Al-Asadi, M., Rodil, S., & Soto, A. (2022). Nanoparticles in Chemical EOR: A Review on Flooding Tests. Nanomaterials, 12(23), 4142. https://doi.org/10.3390/nano12234142 Alilou, M., Shafiei, A., & Hashemi, A. (2023). Surface-modified silica nanoparticles for enhanced oil recovery: Mechanisms and performance evaluation. Journal of Petroleum Science and Engineering, 220, 111205. https://doi.org/10.1016/j.petrol.2022.111205 Gholamzadeh, M., Hemmati-Sarapardeh, M., & Sharifi, A. (2024). Interfacial tension reduction using nitrogen graphene quantum dots with various precursors, molar ratios, and synthesis durations for enhanced oil recovery. Scientific Reports, 14(1), 8328. https://doi.org/10.1038/s41598-024-83282-x Gholinezhad, M., Fakhroueian, Z., & Dehghan Monfared, A. (2022). Effect of surface functionalized silica nanoparticles on interfacial behavior: Wettability, interfacial tension and emulsification. Journal of Molecular Liquids, 345, 118220. https://doi.org/10.1016/j.molliq.2021.118220 Hamza, M. F., Simo, H. S., & Solling, T. I. (2025). Metal oxide nanoparticles for high-salinity enhanced oil recovery: Performance evaluation and mechanisms. Energy & Fuels, 39(2), 845-856. https://doi.org/10.1021/acs.energyfuels.4c04567 Hutin, A., Lima, R., & Quintella, C. M. (2023). Stability of silica nanofluids at high salinity and high temperature. Powders, 2(1), 1-15. https://doi.org/10.3390/powders2010001 Iravani, M., Khalilnezhad, S., & Hashemi, A. (2023). A review on application of nanoparticles for EOR purposes: History and current challenges. Journal of Petroleum Exploration and Production Technology, 13(5), 13202-13220. https://doi.org/10.1007/s13202-022-01606-x Kandiel, E., Attia, M., & El-Amin, M. F. (2025). Nanoparticles in enhanced oil recovery: State-of-the-art review. Journal of Petroleum Exploration and Production Technology, 15(3), 1965-1982. https://doi.org/10.1007/s13202-025-01965-1 Qing, L., Zhang, Y., & Wang, H. (2022). Effect of 2D alpha-zirconium phosphate nanosheets in interfacial tension reduction and wettability alteration: Implications for enhanced oil recovery. SPE Journal, 27(4), 2086-2102. https://doi.org/10.2118/208607-PA Rahman, M., Shirif, E., & Ahmadi, M. (2022). A critical review on nanoparticle-assisted enhanced oil recovery: Introducing scaling approach. International Journal of Nano Dimension, 13(1), 1-15. https://doi.org/10.22034/ijnd.2022.685986 Razavifar, A., Qajar, J., & Riazi, M. (2024). Recent developments, challenges, and prospects of carbon dots for fluid flow investigation in porous media. Petroleum Research, 9(2), 123-135. https://doi.org/10.1016/j.ptlrs.2024.04.004 Rezvani, M., Kazemzadeh, Y., & Riazi, M. (2021). A novel foam formulation by Al2O3/SiO2 nanoparticles for EOR applications: A mechanistic study. Journal of Molecular Liquids, 325, 112730. https://doi.org/10.1016/j.molliq.2020.112730 Salem, A., Mohamed, A., & El-Hoshoudy, A. N. (2024). A comprehensive investigation of nanocomposite polymer flooding at reservoir conditions: New insights into enhanced oil recovery. Journal of Polymers and the Environment, 32(3), 10924-10938. https://doi.org/10.1007/s10924-024-03336-z Soleimani, H. M., & Sadeghi, M. T. (2023). Stability Analysis of Nanoclay Assisted Water Based EOR Methods. Journal of Petroleum Science and Technology, 14(2), 45-62. https://doi.org/10.22078/jpst.2024.5171.1889 Soleimani, H. M., & Sadeghi, M. T. (2025). Experimental investigation of nanoclay performance as an assistant in water based enhanced oil recovery method. Scientific Reports, 15(1), 86530. https://doi.org/10.1038/s41598-025-86530-w Soleimani, H. M., & Sadeghi, M. T. (2024). Experimental evaluation of nanoclay assisted water based EOR method. Journal of Petroleum Exploration and Production Technology, 15(4), 1928-1945. https://doi.org/10.1007/s13202-024-01918-0 Tong, Z., Fan, Y., & Liu, H. (2023). Research progress in nanofluid-enhanced oil recovery technology and mechanism. Molecules, 28(22), 7478. https://doi.org/10.3390/molecules28227478 Xu, Y., Zhong, L., & Zhang, Y. (2022). Synergistic mechanisms between nanoparticles and surfactants: Insight into NP-surfactant interactions. Frontiers in Energy Research, 10, 913360. https://doi.org/10.3389/fenrg.2022.913360 Yin, X., Qiu, Y., & Li, Z. (2025). Modified Fe3O4 nanoparticles for foam stabilization: Mechanisms and applications for EOR. Nanomaterials, 15(5), 395. https://doi.org/10.3390/nano15050395 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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12:29:54","extension":"html","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":72324,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8018070/v1/480d2fd08d5c5bb6b4d07f37.html"},{"id":98515028,"identity":"35e8bfda-2b0f-40b9-afcc-ab8482e95c8c","added_by":"auto","created_at":"2025-12-18 12:29:54","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":26693,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe selection process followed a PRISMA-style workflow\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8018070/v1/c43f851272e57eec40845f97.jpg"},{"id":98515029,"identity":"676eb5b2-3490-482b-b378-4c08d8344602","added_by":"auto","created_at":"2025-12-18 12:29:54","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":27724,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWorkflow of Economic Model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8018070/v1/b7b2cf27566a752e7ad9b884.jpg"},{"id":107522217,"identity":"1a8cb1e5-dbda-4c14-943e-13d6f0459595","added_by":"auto","created_at":"2026-04-22 09:13:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":424582,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8018070/v1/9c824ead-8552-4225-b1b3-0438121790fb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mechanistic Insights Economic Modeling and Research Priorities for Nanoparticle Assisted Enhanced Oil Recovery","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNanoparticle-assisted EOR (NP-EOR) has evolved from proof-of-concept demonstrations to targeted field applications, driven by the unique capabilities of nanomaterials to manipulate fluid-fluid and rock-fluid interactions at the pore scale (Iravani et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tong et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Multiple nanoparticle classes\u0026mdash;including silica (both bare and surface-modified), metal oxides (Al₂O₃, TiO₂, MgO, ZrO₂), iron-oxide/magnetic particles, carbonaceous nanostructures (carbon dots, graphene quantum dots), and nanoclays (particularly montmorillonite)\u0026mdash;have demonstrated effectiveness through four primary mechanisms: wettability alteration, interfacial tension (IFT) reduction, foam/emulsion stabilization, and injected-fluid rheology modification (Al-Asadi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gholamzadeh et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Salem et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent systematic reviews converge on several critical insights. First, mechanistic complementarity significantly enhances recovery outcomes, with combinations of wettability alteration, IFT reduction, and foam/rheology control producing superior results compared to single-mechanism interventions (Xu et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Salem et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Second, nanoparticle stability and transport represent rate-limiting factors, as high temperatures and multivalent ion concentrations promote aggregation and retention, reducing deliverable doses and increasing required injection masses (Hutin et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hamza et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Third, economic viability depends critically on field-scale recovery factors, full-cycle nanoparticle costs, and oil prices, necessitating conservative lab-to-field scaling (α\u0026thinsp;\u0026asymp;\u0026thinsp;0.3\u0026ndash;0.6) until pilot validation (Rahman et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kandiel et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Finally, environmental considerations and produced-water management are essential components of realistic appraisals and regulatory planning (Razavifar et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis review integrates recent experimental advancements, particularly concerning nanoclay systems (Soleimani \u0026amp; Sadeghi, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Soleimani \u0026amp; Sadeghi, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), to provide updated mechanistic understanding, refined transport analysis, and enhanced economic modeling capabilities for NP-EOR applications.\u003c/p\u003e"},{"header":"2. Methods: Literature Selection and Synthesis Framework","content":"\u003cp\u003eWe conducted a systematic review of peer-reviewed experimental, review, and pilot studies published between 2021 and 2025, prioritizing works reporting core-flood, micromodel, dynamic light scattering/zeta potential stability, transport/retention, and field pilot outcomes (Iravani et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Al-Asadi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rezvani et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Data extraction encompassed nanoparticle class and dose, hydrodynamic size and zeta potential in test salinity, core properties (permeability/porosity), test temperature/salinity, measured ΔIFT and contact angle changes, laboratory incremental recovery (ΔRR_lab), retention metrics, and field results where available. For economic modeling, conservative engineering assumptions were applied where numerical cost data were absent, with appropriate sensitivity bounds. Soleimani and colleagues' experimental datasets (Soleimani \u0026amp; Sadeghi, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Soleimani \u0026amp; Sadeghi, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) were utilized to parameterize nanoclay stability versus dose and surfactant system behavior in economic mass-balance and retention submodel. We conducted a systematic review of peer-reviewed experimental, review, and pilot studies published between 2021 and 2025. The selection process followed a PRISMA-style workflow shown Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStep Description:\u003c/p\u003e \u003cp\u003eInitial Search: The process begins with the Identification of approximately 250 potential studies from various databases.\u003c/p\u003e \u003cp\u003eScreening by Title/Abstract: By reviewing the titles and abstracts, the number of studies is reduced to around 120. Studies that are clearly irrelevant to the research topic are excluded.\u003c/p\u003e \u003cp\u003eEligibility Check: The remaining studies (approx. 60) are thoroughly assessed against strict eligibility criteria (NP-EOR topic, publication years 2021–2025, and the presence of lab or field data).\u003c/p\u003e \u003cp\u003eFinal Inclusion for Synthesis: Finally, 30 or more studies that meet all the criteria are selected for the final stage of data analysis and synthesis.\u003c/p\u003e \u003cp\u003eThis standard process ensures that only the most relevant and high-quality studies are included in the final review.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003c/p\u003e "},{"header":"3. Mechanistic Synthesis and Performance Analysis","content":" \u003cp\u003e1. Silica and Surface-Modified Silica Nanoparticles\u003c/p\u003e\u003cp\u003eSilica nanoparticles adsorb onto rock and oil surfaces, creating hydrated films and increasing disjoining pressure to shift wettability toward water-wet conditions and facilitate oil mobilization (Gholinezhad et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Alilou et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Surface modifiers—including silane coupling agents and polymer grafts—enable tuning of hydrophilicity and colloidal stability, though sensitivity to divalent ions and elevated temperatures necessitates optimized coating strategies (Hutin et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e\n\u003ch3\u003e2. Metal Oxides and Inorganic Nanosheets\u003c/h3\u003e\n\u003cp\u003eMetal oxides and layered nanosheets (e.g., zirconium phosphate) provide Lewis acid/base sites that interact with crude oil components to reduce IFT and modify wettability. Several studies report superior IFT reduction for Al₂O₃ and MgO in high-salinity environments compared to SiO₂ (Hamza et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Kandiel et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Qing et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e\n\u003ch3\u003e3. Iron-Oxide (Magnetic)\u003c/h3\u003e\n\u003cp\u003eFe₃O₄ and core-shell magnetic particles stabilize foams and emulsions by forming particulate interfacial layers. Laboratory investigations demonstrate extended foam half-life and potential recyclability at surface facilities, though practical subsurface magnetic recovery remains unproven (Rezvani et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yin et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)Carbonaceous Nanostructures.\u003c/p\u003e\n\u003ch3\u003e4. Carbonaceous Nanostructures\u003c/h3\u003e\n\u003cp\u003eCarbon dots and graphene quantum dots exhibit amphiphilic interfacial activity and significant IFT reduction, even in saline environments (Gholamzadeh et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Razavifar et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Scaling cost-effective synthesis and understanding environmental fate represent ongoing research challenges.\u003c/p\u003e\n\u003ch3\u003e5. Nanoclays: Experimental Advances and Implications\u003c/h3\u003e\n\u003cp\u003eRecent systematic investigations of montmorillonite nanoclay for water-based EOR (Soleimani \u0026amp; Ghasemi, 2024; Soleimani \u0026amp; Sadeghi, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) have yielded critical insights for mechanism understanding and practical implementation. Stability optimization across salt compositions and surfactant systems has identified nanoclay concentrations and surfactant choices that maximize colloidal stability in representative reservoir brines (Soleimani \u0026amp; Sadeghi, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Experimental results demonstrate that optimized nanoclay formulations induce measurable wettability shifts and modest IFT reductions relative to base brines, consistent with enhanced oil mobilization mechanisms (Soleimani \u0026amp; Sadeghi, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Flow and core-flood performance tests reveal that nanoclay-assisted formulations produce incremental oil in oil-wet cores, with recovery magnitude dependent on dose, surfactant presence, and flow regimes (Soleimani \u0026amp; Sadeghi, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Critically, these experiments quantify retention behavior and flow characteristics, showing that optimized nanoclay formulations cause limited permeability impairment, while overdosing increases pore-blocking risks. The combined stability and flooding data enable identification of practical dosing windows where stability, transport, and incremental recovery are balanced\u0026mdash;providing an empirical basis for setting pilot concentrations and parameterizing retention in economic models.\u003c/p\u003e\n\u003ch3\u003e6. Nanoparticle-Surfactant/Polymer Synergies\u003c/h3\u003e\n\u003cp\u003eNanoparticles frequently act synergistically with surfactants and polymers, reducing surfactant adsorption, stabilizing foams, and modifying polymer rheology under saline conditions (Xu et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kandiel et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Experimental findings on surfactant-nanoclay systems (Soleimani \u0026amp; Sadeghi, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) demonstrate how surfactant-assisted stabilization can tune nanoclay behavior for EOR applications, supporting broader literature on nanoparticle-surfactant synergies.\u003c/p\u003e\n\u003ch3\u003e7. Transport, Retention, and Pore-Scale Considerations\u003c/h3\u003e\n\u003cp\u003eRetention (through sorption and straining) and aggregation determine the fraction of injected nanoparticles that reach target zones (Rahman et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Recent experimental studies have quantified retention behavior In core tests (Soleimani \u0026amp; Sadeghi, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), providing retention curves that can be directly incorporated into injection mass-balance calculations and economic models, significantly improving delivered dose estimation realism.comparartive table of nanoparticle classes is listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparartive Table of Nanoparticle Classes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNanoparticle Class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDominant Mechanism(s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTypical Dose Range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eΔIFT Reduction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWettability Alteration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eΔRR_lab (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKey Limitations\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSilica (bare/modified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWettability alteration, disjoining pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u0026ndash;0.1 wt%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStrong (oil-wet \u0026rarr; water-wet)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSensitive to divalent ions, aggregation at high T\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetal oxides (Al₂O₃, MgO, TiO₂, ZrO₂)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIFT reduction, wettability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u0026ndash;0.05 wt%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCost, surface reactivity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIron-oxide (Fe₃O₄)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFoam/emulsion stabilization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u0026ndash;0.05 wt%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u0026ndash;moderate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLimited\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSubsurface magnetic recovery unproven\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbonaceous (dots, GQDs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIFT reduction, amphiphilic activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u0026ndash;0.02 wt%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVery high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20\u0026ndash;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCost-effective synthesis, environmental fate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNanoclays (montmorillonite)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWettability alteration, modest IFT reduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u0026ndash;0.2 wt%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u0026ndash;moderate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOverdosing \u0026rarr; pore blocking, retention risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"4. Laboratory-to-Field Translation: Protocol and Pilot Design Implications","content":"\u003cp\u003eBased on integrated literature and experimental evidence (Soleimani \u0026amp; Sadeghi, 2023; Soleimani \u0026amp; Sadeghi, 2024, 2025), we recommend a structured protocol for translating nanoclay and broader nanoparticle laboratory results to pilot applications:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eStability Triage: Perform dynamic light scattering and zeta potential measurements in candidate reservoir brines (including divalent ions) and screen surfactant co-formulations to identify stable nanofluid windows (Soleimani \u0026amp; Sadeghi, 2023).\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Core Tests with Retention Tracking: Conduct core-flood experiments with upstream and downstream sampling and nanoparticle-label/tracer co-injection to derive retention curves and assess potential permeability changes (Soleimani \u0026amp; Sadeghi, 2025).\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Practical Dosing Window Determination: Utilize static IFT/contact angle measurements and dynamic core results to select the lowest effective nanoparticle concentration that achieves target wettability/IFT shifts while limiting retention and formation damage (Soleimani \u0026amp; Sadeghi, 2024).\u003c/li\u003e\n \u003cli\u003ePilot Configuration: Implement staged injection with monitoring wells, nanoparticle analysis in produced water, and contingency plans for produced-water treatment, incorporating environmental monitoring and multi-year surveillance.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"5. Advanced Economic Modeling Framework ","content":"\u003cp\u003eWe present a enhanced economic modeling framework that integrates empirical retention and dose data (Soleimani \u0026amp; Sadeghi, 2024; Soleimani \u0026amp; Sadeghi, 2024, 2025) to provide realistic cost and delivered dose inputs for viability assessment.Workflow of Economic model is shown Fig2..\u003c/p\u003e\n\u003cp\u003eProcess Description:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eRetention \u0026amp; Stability Data: Starts with collecting laboratory and field data on nanoparticle retention and stability - fundamental performance metrics.\u003c/li\u003e\n \u003cli\u003eMass Balance: Analyzes the relationship between injected mass (M\u003csub\u003einj\u003c/sub\u003e) and effectively delivered mass (M\u003csub\u003edelivered\u003c/sub\u003e) to understand transport efficiency.\u003c/li\u003e\n \u003cli\u003eCost Components: Breaks down the economic factors including nanoparticle costs (C\u003csub\u003enp\u003c/sub\u003e), logistics, and treatment expenses.\u003c/li\u003e\n \u003cli\u003eMonte Carlo Simulation: Runs probabilistic analysis using key variables:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e· ΔRR\u003csub\u003elab\u003c/sub\u003e: Recovery factor improvement from lab data\u003c/p\u003e\n\u003cp\u003e· α: Scaling factor\u003c/p\u003e\n\u003cp\u003e· f\u003csub\u003eret\u003c/sub\u003e: Retention factor\u003c/p\u003e\n\u003cp\u003e· P\u003csub\u003eoil\u003c/sub\u003e: Oil price\u003c/p\u003e\n\u003col start=\"5\"\u003e\n \u003cli\u003eNPV/IRR Probability Distributions: Generates probability distributions for Net Present Value and Internal Rate of Return to assess economic viability under uncertainty.\u003c/li\u003e\n \u003cli\u003eDecision Support: Provides the final output to guide decisions about proceeding with pilot testing or full field deployment based on integrated technical and economic analysis\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e5.1 Mass-Balance and Retention Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe delivered mass at target distance is calculated as:\u003c/p\u003e\n\u003cp\u003eM\u003csub\u003edelivered\u003c/sub\u003e= C\u003csub\u003etarget\u003c/sub\u003e × PV\u003csub\u003etarget\u003c/sub\u003e × φ\u003csub\u003etarget\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003ewhere φ_target represents pore volume at target.The required injected mass is:\u003c/p\u003e\n\u003cp\u003eM \u003csub\u003einjected\u0026nbsp;\u003c/sub\u003e= M\u003csub\u003edelivered\u003c/sub\u003e / (1 - f\u003csub\u003eret\u003c/sub\u003e)\u003c/p\u003e\n\u003cp\u003eExperimental retention curves(Soleimani \u0026amp; Sadeghi, 2025) enable estimation of f\u003csub\u003eret\u003c/sub\u003e as a function of injected pore volumes and brine composition, allowing precise sizing of M\u003csub\u003einjected\u003c/sub\u003e and consequent nanoparticle material costs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.2 Comprehensive Cost Components\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFull-cycle nanoparticle cost (per barrel injected) should include:\u003c/p\u003e\n\u003cp\u003e·\u0026nbsp;Nanoparticle material cost (raw nanoclay + modification)\u003c/p\u003e\n\u003cp\u003e·\u0026nbsp;Formulation and mixing costs (surfactant, pH/salinity adjustment)\u003c/p\u003e\n\u003cp\u003e·\u0026nbsp;Logistics and injection preparation\u003c/p\u003e\n\u003cp\u003e·\u0026nbsp;Produced water nanoparticle monitoring and treatment allocation\u003c/p\u003e\n\u003cp\u003e·\u0026nbsp;Environmental monitoring and contingency provisioning\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.3 Integrated NPV/IRR Modeling with Probabilistic Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur economic model incorporates Net Present Value (NPV) and Internal Rate of Return (IRR) calculations with Monte Carlo sampling for ΔRR\u003csub\u003elab\u003c/sub\u003e, scaling factors (α), retention fractions (f\u003csub\u003eret\u003c/sub\u003e), nanoparticle costs (C\u003csub\u003enp\u003c/sub\u003e), and oil prices (P\u003csub\u003eoil\u003c/sub\u003e). The model outputs probability distributions for NPV\u0026gt;0, IRR, and tornado charts for sensitivity analysis. Scenario analysis includes staged reinjection to offset retention losses and evaluate economic impact.\u003c/p\u003e"},{"header":"6. Discussion and Research Agenda","content":"\u003cp\u003eIntegrating recent experimental results (Soleimani \u0026amp; Sadeghi, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Soleimani \u0026amp; Sadeghi, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) strengthens the practical case for nanoclay as a cost-effective nanoparticle family with genuine EOR potential when properly formulated and injected. Stability mapping and retention quantification reduce two critical uncertainties: deliverable nanoparticle concentration at distance and dosing windows that balance efficacy with formation safety. Economic modeling demonstrates that nanoclay programs can achieve attractiveness under specific conditions: stable formulated dispersions in reservoir brine, measured retention below critical thresholds (cumulative f\u003csub\u003eret\u003c/sub\u003e \u0026lt; 20–30% over transport path), and achievable ΔRR\u003csub\u003elab\u003c/sub\u003e that yields ΔRR\u003csub\u003efield\u003c/sub\u003e ≥ 0.03–0.05 after conservative scaling.\u003c/p\u003e \u003cp\u003eRemaining challenges include long-term stability under reservoir temperature and chemical conditions, potential unforeseen formation interactions at field scale, and environmental/regulatory constraints on surfactant and nanoparticle discharge. Future pilot work should prioritize retention measurement and produced-water fate studies to reduce investment risk.research gaps and priorities is listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResearch Gaps and Priorities\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResearch Gap\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhy It Matters\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRecommended Action\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLong-term stability under reservoir T/P/salinity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMost studies ≤ 6 months\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMulti-year stability tests with real brines\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetention quantification at field scale\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLab data may not scale\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTracer/label pilots with retention profiling\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProduced-water fate \u0026amp; treatment\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironmental/regulatory risk\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDevelop monitoring \u0026amp; treatment protocols\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandardized reporting\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent heterogeneity prevents comparison\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdopt unified metrics (ΔRR, zeta, IFT, contact angle)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic scaling\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLab-to-field α uncertain\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMore pilot data to refine α (0.3–0.6 → validated ranges)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCost-effective synthesis\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh cost limits deployment\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGreen/low-cost synthesis of nanoclay \u0026amp; hybrids\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003c/p\u003e"},{"header":"7. Conclusions and Recommendations","content":"\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eNanoclay (montmorillonite) is supported by experimental evidence (Soleimani \u0026amp; Sadeghi, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Soleimani \u0026amp; Sadeghi, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) as a practical, cost-effective nanoparticle class for water-based EOR when co-formulated with appropriate surfactants and dosed within empirically derived windows.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eStability screening (DLS/zeta), core-flood retention profiling, and tracer/label pilots are essential preconditions for scaling nanoclay EOR; Soleimani and colleagues' datasets provide concrete starting points for dosing and retention expectations.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eEconomic viability depends critically on deliverable ΔRR\u003csub\u003efield\u003c/sub\u003e, retention (f\u003csub\u003eret\u003c/sub\u003e), nanoparticle costs (C\u003csub\u003enp\u003c/sub\u003e), and oil price; using empirical retention curves significantly improves realism of NPV/IRR estimates (Rahman et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kandiel et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eResearch priorities include long-term stability at reservoir temperature/salinity, produced-water fate and treatment, pilot designs with nanoparticle labeling, and development of low-cost, low-impact formulation strategies.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for conducting this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all the researchers whose work contributed to the literature reviewed in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.This article is a comprehensive review of existing literature and does not involve any direct human participation, animal experiments, or clinical trials. Therefore, ethical approval and consent to participate are not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This manuscript does not contain any individual person's data in any form (including any individual details, images, or videos). As a comprehensive review article, it is based solely on analysis and synthesis of previously published literature, and thus no consent for publication from individuals is required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.This study is a literature review and does not involve any clinical trial.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAl-Asadi, M., Rodil, S., \u0026amp; Soto, A. (2022). Nanoparticles in Chemical EOR: A Review on Flooding Tests. Nanomaterials, 12(23), 4142. https://doi.org/10.3390/nano12234142\u003c/li\u003e\n\u003cli\u003eAlilou, M., Shafiei, A., \u0026amp; Hashemi, A. (2023). Surface-modified silica nanoparticles for enhanced oil recovery: Mechanisms and performance evaluation. Journal of Petroleum Science and Engineering, 220, 111205. https://doi.org/10.1016/j.petrol.2022.111205\u003c/li\u003e\n\u003cli\u003eGholamzadeh, M., Hemmati-Sarapardeh, M., \u0026amp; Sharifi, A. (2024). Interfacial tension reduction using nitrogen graphene quantum dots with various precursors, molar ratios, and synthesis durations for enhanced oil recovery. Scientific Reports, 14(1), 8328. https://doi.org/10.1038/s41598-024-83282-x\u003c/li\u003e\n\u003cli\u003eGholinezhad, M., Fakhroueian, Z., \u0026amp; Dehghan Monfared, A. (2022). Effect of surface functionalized silica nanoparticles on interfacial behavior: Wettability, interfacial tension and emulsification. Journal of Molecular Liquids, 345, 118220. https://doi.org/10.1016/j.molliq.2021.118220\u003c/li\u003e\n\u003cli\u003eHamza, M. F., Simo, H. S., \u0026amp; Solling, T. I. (2025). Metal oxide nanoparticles for high-salinity enhanced oil recovery: Performance evaluation and mechanisms. Energy \u0026amp; Fuels, 39(2), 845-856. https://doi.org/10.1021/acs.energyfuels.4c04567\u003c/li\u003e\n\u003cli\u003eHutin, A., Lima, R., \u0026amp; Quintella, C. M. (2023). Stability of silica nanofluids at high salinity and high temperature. Powders, 2(1), 1-15. https://doi.org/10.3390/powders2010001\u003c/li\u003e\n\u003cli\u003eIravani, M., Khalilnezhad, S., \u0026amp; Hashemi, A. (2023). A review on application of nanoparticles for EOR purposes: History and current challenges. Journal of Petroleum Exploration and Production Technology, 13(5), 13202-13220. https://doi.org/10.1007/s13202-022-01606-x\u003c/li\u003e\n\u003cli\u003eKandiel, E., Attia, M., \u0026amp; El-Amin, M. F. (2025). Nanoparticles in enhanced oil recovery: State-of-the-art review. Journal of Petroleum Exploration and Production Technology, 15(3), 1965-1982. https://doi.org/10.1007/s13202-025-01965-1\u003c/li\u003e\n\u003cli\u003eQing, L., Zhang, Y., \u0026amp; Wang, H. (2022). Effect of 2D alpha-zirconium phosphate nanosheets in interfacial tension reduction and wettability alteration: Implications for enhanced oil recovery. SPE Journal, 27(4), 2086-2102. https://doi.org/10.2118/208607-PA\u003c/li\u003e\n\u003cli\u003eRahman, M., Shirif, E., \u0026amp; Ahmadi, M. (2022). A critical review on nanoparticle-assisted enhanced oil recovery: Introducing scaling approach. International Journal of Nano Dimension, 13(1), 1-15. https://doi.org/10.22034/ijnd.2022.685986\u003c/li\u003e\n\u003cli\u003eRazavifar, A., Qajar, J., \u0026amp; Riazi, M. (2024). Recent developments, challenges, and prospects of carbon dots for fluid flow investigation in porous media. Petroleum Research, 9(2), 123-135. https://doi.org/10.1016/j.ptlrs.2024.04.004\u003c/li\u003e\n\u003cli\u003eRezvani, M., Kazemzadeh, Y., \u0026amp; Riazi, M. (2021). A novel foam formulation by Al2O3/SiO2 nanoparticles for EOR applications: A mechanistic study. Journal of Molecular Liquids, 325, 112730. https://doi.org/10.1016/j.molliq.2020.112730\u003c/li\u003e\n\u003cli\u003eSalem, A., Mohamed, A., \u0026amp; El-Hoshoudy, A. N. (2024). A comprehensive investigation of nanocomposite polymer flooding at reservoir conditions: New insights into enhanced oil recovery. Journal of Polymers and the Environment, 32(3), 10924-10938. https://doi.org/10.1007/s10924-024-03336-z\u003c/li\u003e\n\u003cli\u003eSoleimani, H. M., \u0026amp; Sadeghi, M. T. (2023). Stability Analysis of Nanoclay Assisted Water Based EOR Methods. Journal of Petroleum Science and Technology, 14(2), 45-62. https://doi.org/10.22078/jpst.2024.5171.1889\u003c/li\u003e\n\u003cli\u003eSoleimani, H. M., \u0026amp; Sadeghi, M. T. (2025). Experimental investigation of nanoclay performance as an assistant in water based enhanced oil recovery method. Scientific Reports, 15(1), 86530. https://doi.org/10.1038/s41598-025-86530-w\u003c/li\u003e\n\u003cli\u003eSoleimani, H. M., \u0026amp; Sadeghi, M. T. (2024). Experimental evaluation of nanoclay assisted water based EOR method. Journal of Petroleum Exploration and Production Technology, 15(4), 1928-1945. https://doi.org/10.1007/s13202-024-01918-0\u003c/li\u003e\n\u003cli\u003eTong, Z., Fan, Y., \u0026amp; Liu, H. (2023). Research progress in nanofluid-enhanced oil recovery technology and mechanism. Molecules, 28(22), 7478. https://doi.org/10.3390/molecules28227478\u003c/li\u003e\n\u003cli\u003eXu, Y., Zhong, L., \u0026amp; Zhang, Y. (2022). Synergistic mechanisms between nanoparticles and surfactants: Insight into NP-surfactant interactions. Frontiers in Energy Research, 10, 913360. https://doi.org/10.3389/fenrg.2022.913360\u003c/li\u003e\n\u003cli\u003eYin, X., Qiu, Y., \u0026amp; Li, Z. (2025). Modified Fe3O4 nanoparticles for foam stabilization: Mechanisms and applications for EOR. Nanomaterials, 15(5), 395. https://doi.org/10.3390/nano15050395\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"nanoparticles, enhanced oil recovery, nanoclay, wettability alteration, interfacial tension, economic modeling, lab-to-field scaling","lastPublishedDoi":"10.21203/rs.3.rs-8018070/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8018070/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis comprehensive review synthesizes recent advances (2021\u0026ndash;2025) in nanoparticle-assisted enhanced oil recovery (NP-EOR), integrating mechanistic understanding, transport phenomena, and economic modeling. Through systematic analysis of more than 30 experimental and field studies, we establish a refined taxonomy of nanoparticle classes\u0026mdash;including silica, metal oxides, iron-oxide, carbonaceous nanostructures, and nanoclays\u0026mdash;based on their distinct mechanisms and performance characteristics. The integration of recent experimental studies on nanoclay systems (Soleimani \u0026amp; Sadeghi, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Soleimani \u0026amp; Sadeghi, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) provides critical insights into stability optimization, practical dosing windows, and retention behavior. Our analysis demonstrates that synergistic mechanisms combining wettability alteration, interfacial tension reduction, and rheology control yield maximum incremental recovery (25\u0026ndash;45% in laboratory settings). We present a sophisticated economic modeling framework incorporating Net Present Value (NPV) and Internal Rate of Return (IRR) analyses with Monte Carlo simulation, accounting for retention losses and scaling factors (Rahman et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kandiel et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Key findings indicate that economic viability is highly sensitive to field-scale recovery factors (ΔRR\u003csub\u003efield\u003c/sub\u003e), full-cycle nanoparticle costs, and oil price volatility, with lab-to-field scaling factors (α\u0026thinsp;\u0026asymp;\u0026thinsp;0.3\u0026ndash;0.6) requiring conservative estimation until pilot validation. This is the first review to integrate empirical nanoclay retention and stability datasets into a probabilistic techno-economic framework, providing a structured protocol for pilot translation and field implementation.\u003c/p\u003e","manuscriptTitle":"Mechanistic Insights Economic Modeling and Research Priorities for Nanoparticle Assisted Enhanced Oil Recovery","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-18 12:29:49","doi":"10.21203/rs.3.rs-8018070/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"650da12a-fbda-403b-b0c3-5582e113b6d8","owner":[],"postedDate":"December 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-22T09:11:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-18 12:29:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8018070","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8018070","identity":"rs-8018070","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}