An Analytical Solution to Interpret Active Ion Transport During Chemically‐Tuned Waterflooding Process in High‐Temperature Carbonate Rocks
Bibliographic record
Abstract
Abstract Recent studies on carbonate reservoirs suggest that modifying the injected brine chemistry leads to a competition amongst ions at the rock surface sites, causing alteration in the rock wettability. As such, the potential increase in oil recovery heavily relies on the relationship between the ion transport and chemical reaction. In this work, we highlighted the three processes that govern this relationship as advection by fluid transport, hydrodynamic dispersion resulting in spreading, and sorption of the ions to the rock. An analytical solution to the mathematical equations describing solute reactive transport was derived based on the advection‐reaction‐dispersion equation (ARDE). Our model was first used to replicate produced ion histories from single phase experiments to emphasize the impact of the active ions on rock surface sites. We then studied the movement of the active ions by predicting the breakthrough composition of different ions during oil‐brine displacement experiments and evaluated the impact of the transport and reaction parameters on the wettability change. The wettability change profile was captured in terms of the sorbed ion concentration, where a high retardation was observed for the active ions. The observed oil recoveries in a data set of two‐phase experiments were correlated to the resultant effect of the rock surface chemistry. This study highlights the importance of rock surface chemistry on wettability change and oil production from carbonate rocks.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".