Comprehensive experimental study of multivariable oil–brine–rock interactions: impact of brine–rock chemistry and crude oil polarity on enhanced oil recovery in carbonate reservoirs
Bibliographic record
Abstract
Reservoir wettability alteration is a vital method for enhancing oil recovery, especially at the pore scale, where it plays a key role in optimizing extraction and minimizing risks from chemical and water flooding. Among different mechanisms, surface charge stands out as a promising and practical approach. This study examines the effects of seven smart water compositions (SW0SO4, SW2SO4, SW4SO4, SW2Mg, SW2Ca, SW25d, SW50d), two heptol ratios, and varied asphaltene concentrations on oil recovery in carbonate rocks (calcite and dolomite). Surface charge and interfacial tension were assessed at both oil/brine and rock/brine interfaces using zeta potential, interfacial tension, and elasticity measurements. The results show that oil/water zeta potential varied with asphaltene content (0.2%, 4%, 9%), heptol ratio (1:5, 1:40), and brine composition, even under constant pH and ionic strength. Lower heptol ratio led to increased negative surface charge on asphaltenes, and higher asphaltene content increased interfacial tension in different brines. Low-salinity and sulphate-rich brines enhanced interfacial elasticity, increasing oil droplet coalescence time and reducing adhesive forces, which improved oil mobility. The study also revealed distinct differences in surface charge between dolomite and calcite in the same brine. Imbibition tests show that oil recovery correlates with surface charge alteration mechanisms during smart water injection. A higher total zeta potential is associated with enhanced oil recovery, emphasizing the important role of surface charge in influencing oil production. Notably, the total zeta potential values align with oil recovery, suggesting that stronger static repulsion between oil droplets and the rock surface significantly improves oil production. These results highlight the relevance of surface charge in EOR processes and support the development of charge-based recovery models.
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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) | 0.000 | 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) | 0.001 | 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".