Evaluation of the simultaneous use of α-Fe <sub>2</sub> O <sub>3</sub> nanoparticles and polyacrylamide polymer as an enhanced oil recovery method
Bibliographic record
Abstract
Currently, polymers and nanoparticles (NPs) have garnered significant attention in the petroleum industry due to their potential to address critical production challenges, such as declining reservoir pressure, high oil viscosity, and water breakthrough. This study investigates the synergistic effect of polyacrylamide (PAM) and α-Fe₂O₃ NPs on enhanced oil recovery (EOR) from carbonate rocks. Various experimental methods, including core flooding, contact angle measurements, interfacial tension (IFT) analysis, and viscosity assessments, were conducted under different scenarios to evaluate the performance of these fluids. The results demonstrated that adding α-Fe₂O₃ NPs to the PAM solution increased the viscosity of the aqueous phase, leading to more efficient oil displacement. Additionally, α-Fe₂O₃ NPs effectively altered the wettability of the rock, reducing the contact angle from 132° to 102° and decreasing the IFT from 24 to 13 dyne/cm, contributing to improved oil recovery. The 48% reduction in IFT was attributed to the adsorption of NPs at the oil-water interface, which lowered the surface energy. Furthermore, the hydrophobicity of the carbonate rock decreased by 24%, as indicated by the reduced contact angle. This wettability shift is linked to the formation of a more homogeneous oil-water interface, stabilized by the high surface area of the NPs. Core flooding experiments revealed an 18% increase in ultimate oil recovery with the addition of α-Fe₂O₃ NPs to the PAM solution. These findings highlight the potential of combining PAM and α-Fe₂O₃ NPs as an effective EOR method for field-scale applications, offering a promising solution to enhance oil recovery in reservoirs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".