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Open research questions in Enhanced Oil Recovery Techniques

34 unresolved questions extracted from the limitations and future-work sections of 230 Enhanced Oil Recovery Techniques papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • Based on the review, it is proposed that the development of green additives and nanomaterials with controllable polymeric structure should be focused on, and the environmental protection and long-term adaptation stability of additives in a complex marine environment should be studied by combining molecular simulation with microscopic characterization.

    A critical review of mechanism research and application of chemical additives to improve salt resistance of water-based drilling fluid · 2026 · DOI

  • Although inter-fracture CO2 flooding has demonstrated considerable potential for enhanced oil recovery (EOR), the coupled effects of key operational parameters on reservoir pressure evolution, fracture–matrix mass transfer, and oil mobilization remain inadequately understood.

    Effects of Injection–Production Parameters in Inter-Fracture Gas Injection for Horizontal Wells of the Changqing Yuan 284 Tight Oil Reservoir · 2026 · DOI

  • This approach follows established ML validation protocols and provides comprehensive diagnostic transparency, and directly addresses a recognized gap in the literature: the absence of transparent, model-agnostic validation and error characterization for CA prediction in EOR-relevant systems.

    A robust machine learning framework for predicting contact angle in nano-assisted chemical EOR · 2026 · DOI

  • Low-salinity waterflooding (LSWF) offers a promising enhanced oil recovery (EOR) solution by altering wettability through controlled brine chemistry, yet its application in tight formations remains underexplored, and predictive tools for rapid screening are lacking.

    Predictive Machine Learning Framework for Ion-Tuned Low-Salinity Enhanced Oil Recovery in Tight Niger Delta Formations · 2026 · DOI

  • Abstract Low-salinity waterflooding (LSWF) combined with nanoparticle (NP)-enhanced formulations shows promise for Middle East reservoirs, yet the relative contributions of salinity, rock mineralogy, and interfacial tension (IFT) to wettability alteration (WA) remain poorly quantified.

    Mineralogy and Salinity Control Nano-Assisted Wettability Alteration in Sandstone and Carbonate Reservoirs: Contact Angle Screening for Middle East EOR Optimization · 2026 · DOI

  • The study reconstructed concentration profiles for inclined suspensions using TARG (time-averaged radiography) measurements, but the framework has not been extended to predict behavior under dynamic conditions such as flow reversal, oscillatory motion, or time-varying inclination angles relevant to drilling operations.

    Prediction of sedimentation concentration profiles in inclined suspension systems: A data-driven neural network framework · 2026 · DOI

  • The gamma-ray attenuation measurements demonstrate localized relative errors during the passage of the settling front where concentration–time curves exhibit steep gradients; methods to reduce these transient region discrepancies in spatiotemporal concentration profiles using advanced signal processing or improved ANN architectures (e.g., physics-informed neural networks) have not been evaluated.

    Prediction of sedimentation concentration profiles in inclined suspension systems: A data-driven neural network framework · 2026 · DOI

  • Experimental validation was conducted at bench-scale geometry with a single fluid matrix and particle type; applicability of the ANN-based framework to field-scale directional drilling operations with varied fluid rheologies, particle size distributions, and higher solids concentrations requires investigation.

    Prediction of sedimentation concentration profiles in inclined suspension systems: A data-driven neural network framework · 2026 · DOI

  • The present work intentionally adopts a purely data-driven approach without integrating hindered-settling equations or hybrid physics–machine learning formulations; hybrid mechanistic-ML models combining fundamental sedimentation theory with neural networks for improved interpretability across broader operating conditions in inclined systems remain undeveloped.

    Prediction of sedimentation concentration profiles in inclined suspension systems: A data-driven neural network framework · 2026 · DOI

  • The study employed separate neural networks trained for each inclination angle to preserve regime-specific sedimentation characteristics; development of a unified ANN architecture that integrates inclination angle as an input parameter across multiple angles in inclined suspension systems has not been explored.

    Prediction of sedimentation concentration profiles in inclined suspension systems: A data-driven neural network framework · 2026 · DOI

  • The ANN model was trained and tested exclusively within a single experimental domain (glycerin–water 92% v/v, glass microspheres 212–800 µm, 20% v/v solids), meaning high accuracy reflects interpolation rather than extrapolation; generalization to other rheologies, particle morphologies, volumetric concentrations, or field-scale conditions in inclined suspension systems remains untested.

    Prediction of sedimentation concentration profiles in inclined suspension systems: A data-driven neural network framework · 2026 · DOI

  • No comparison or ensemble strategy was explored combining the three best-performing models (GRNN, CFNN-LM, RBF-ACO) to potentially improve robustness or reduce the AAPRE further, nor was uncertainty quantification implemented for MMP predictions in critical reservoir conditions.

    Interpretable machine learning-based modelling of minimum miscibility pressure in hydrocarbon gas injection processes · 2026 · DOI

  • The developed ANN models were trained on a dataset with unspecified ranges for reservoir temperature, injected gas critical temperature, and C5+ molecular weight; the exact input space boundaries and extrapolation behavior beyond these ranges for MMP prediction remain undefined.

    Interpretable machine learning-based modelling of minimum miscibility pressure in hydrocarbon gas injection processes · 2026 · DOI

  • The paper does not compare the GRNN model's MMP predictions against slim-tube experimental data or other laboratory-validated MMP determination methods (e.g., Fast-SLIM tube mentioned in references) to establish the practical applicability of the machine learning model for field decision-making in EOR processes.

    Interpretable machine learning-based modelling of minimum miscibility pressure in hydrocarbon gas injection processes · 2026 · DOI

  • The leverage analysis using William's plot detected only one suspected data point out of the entire dataset, but no investigation was conducted into whether this outlier represents a genuine physical anomaly in MMP behavior or a measurement error that warrants exclusion or separate sub-model development.

    Interpretable machine learning-based modelling of minimum miscibility pressure in hydrocarbon gas injection processes · 2026 · DOI

  • While the paper identifies MW_C5+ and Tc,ave_gas as the most influential variables through sensitivity analysis and SHAP analysis, the mechanistic relationship between these molecular properties and MMP behavior in the context of mass transfer and phase equilibrium during gas injection is not theoretically explained or validated experimentally.

    Interpretable machine learning-based modelling of minimum miscibility pressure in hydrocarbon gas injection processes · 2026 · DOI

  • The inspiration for gas hydrate production using the CHSI solvent preparation method (high temperature CO₂ extraction of methane) is proposed in Section 3.5 but lacks experimental or simulation validation. Laboratory or numerical studies comparing high temperature CO₂ injection against existing methane extraction methods (depressurization, thermal stimulation, CO₂ replacement) in hydrate sediments are needed.

    Cyclic hot solvent injection: An advantageous injection method compared with mixture solvent in the solvent based heavy oil mining process · 2026 · DOI

  • The temperature-viscosity regression curve (Fig. S1) for the specific heavy oil sample (2601 cP at 20.4°C) is developed from limited temperature data points in Table 1. The applicability of this regression model to heavy oils with different chemical compositions, aromatic content, and API grades is not addressed and requires validation with multiple oil samples.

    Cyclic hot solvent injection: An advantageous injection method compared with mixture solvent in the solvent based heavy oil mining process · 2026 · DOI

  • The NPV economic analysis for CHSI versus CSI methods (cold solvent injection) is based on assumed parameters extrapolated from laboratory-scale tests to field conditions. The sensitivity of NPV projections to uncertainties in well drilling costs, solvent injection efficiency, and oil production rates at field scale remains unquantified and requires validation through pilot test data.

    Cyclic hot solvent injection: An advantageous injection method compared with mixture solvent in the solvent based heavy oil mining process · 2026 · DOI

  • The discrepancy between regressed viscosity (1751 cP) and measured viscosity (710 cP) at the end of Phase 2 in CHSI is attributed to asphaltene precipitation, but the quantitative relationship between asphaltene content reduction and viscosity reduction rate has not been empirically modeled. This relationship requires systematic investigation across varying temperature and solvent injection cycles.

    Cyclic hot solvent injection: An advantageous injection method compared with mixture solvent in the solvent based heavy oil mining process · 2026 · DOI

  • The asphaltene precipitation mechanism during cyclic hot solvent injection (CHSI) is described only at a basic level. The specific relationship between solvent condensation component at the oil-solvent interface and the extent of asphaltene deposition needs quantitative characterization across different reservoir temperatures and solvent compositions to predict permeability reduction in field applications.

    Cyclic hot solvent injection: An advantageous injection method compared with mixture solvent in the solvent based heavy oil mining process · 2026 · DOI

  • Being unable to produce more than 35%-55% of the original oil in place after the application of primary and secondary oil recovery techniques gave rise to the need for developing enhanced oil recovery techniques, hence the necessity to carry out further research on MEOR arose.

    A Review Paper on Microbial Enhanced Oil Recovery Applications Projects · 2018

  • Marine drilling fluid shows insufficient performance stability in high salt environments, and the research on the synergistic mechanism and compatibility effect of different chemical additives under compound salt conditions is insufficient.

    A critical review of mechanism research and application of chemical additives to improve salt resistance of water-based drilling fluid · 2026 · DOI

  • The physicochemical mechanisms and numerical characterization of amine-ether gemini surfactant emulsion flooding remain insufficient, limiting its field application in low-permeability reservoirs.

    A Refined Numerical Simulation Method for Amine-Ether Gemini Surfactant Emulsion Flooding · 2026 · DOI

  • Although horizontal wells can improve oil displacement efficiency by providing a larger contact area with the formation, systematic studies on the displacement behavior and key influencing factors of polymer flooding in opposed horizontal wells are still lacking.

    Oil Displacement Behavior of Polymer Flooding in Horizontal Well Patterns: Experimental and Numerical Simulation Approaches · 2026 · DOI

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34 open questions have been extracted from the limitations and future-work passages of 230 Enhanced Oil Recovery Techniques papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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