Optimum salinity brine and surfactant interaction with crude oil and carbonated rock at fluid–fluid and rock‐fluid interfaces: Evaluating ion‐specific effects on the system
Bibliographic record
Abstract
Abstract The smart water injection (SWI) method is derived from low salinity water flooding, which aims to increase oil production. The complexity and heterogeneity of carbonate reservoirs require further investigation. Understanding the active mechanisms during SWI is crucial for designing the injected water composition to enhance the efficiency of the method. However, there is a research gap regarding the dominant factors that influence the performance of SWI, particularly both the ‘fluid–fluid’ and ‘rock–fluid’ interactions, simultaneously. To address this issue, the article through new insight into ions, solutions, and tests tries to enhance the precision of the obtained results by optimizing the solutions by changing the potential determining ions (PDI) in fixed ionic strength through contact angle measurements. Furthermore, by using a cationic surfactant, which has not been previously used in low‐salinity water flooding experiments, surfactant‐free solutions, and proper tests for each mechanism, including Zeta potential, pH measurements, interfacial tension, vial test, high‐resolution microscopy, and Karl Fischer titration, interactions on fluid–fluid and rock‐fluid interfaces were tested and investigated. The results were validated through core flood tests. The study found that coordination was observed between multivalent ion exchange and water in oil micro‐dispersion, which are two current mechanisms in both micro‐ and macro‐scale media. In surfactant‐free solutions, the effect of ions was discerned in the order of SO 4 2− , Mg 2+ , and Ca 2+ . The formation of micro‐emulsions and the IFT reduction by ionic pairs of surfactants significantly increased oil recovery by up to 65%. The effective ions facilitating this performance were ranked as follows: SO 4 2− > Ca 2+ > Mg 2+ .
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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 | 0.001 |
| 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".