Understanding the Chemical Mechanisms for Low Salinity Waterflooding
Bibliographic record
Abstract
Abstract Low salinity water (LSW) is reported to improve oil recovery (IOR) significantly in sandstone and carbonate core experiments. Ranges of IOR vary significantly depending on the chemical composition of brine, oil and cores. We previously developed a process-based and predictive model that explicitly includes the chemical interactions between crude oil, brine, and the carbonate surface that alter rock wettability. In this research, we improve the developed model to include explicitly the acid/base interaction and ion-binding interaction of crude oil adsorption. The wettability alteration is quantified by the surface concentrations of adsorbed carboxylic acids, which is a result of aqueous and surface reactions. The total concentrations of aqueous and surface species are varied individually and together over a large range while precipitation constraints are also included. The wettability is for a variety of brine compositions used in experiments. The wettability depends strongly on the concentration of Ca2+, Mg2+ and SO42−, as well as the total salinity. Including the acid/base interaction can explain the wettability alteration trend when Ca2+, Mg2+ and SO42− concentrations are significant. Including the ion-binding through Ca2+ can better explain the wettability alteration trend when diluted formation water or seawater is injected. We can reproduce the wettability alteration trend reported in experiments from different sources by combining the acid/base and ion-binding mechanisms and considering the irreversibility of the carboxylic adsorption reactions.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".