Experimental Investigation of Wettability Alteration in Oil-Wet Reservoirs Containing Heavy Oil
Bibliographic record
Abstract
Summary Solvent injection is an effective way to lower the viscosity of heavy oil and is considered when thermal techniques are not practically applicable. However, for the economic success of the method, large fractions of the injected solvent must be recovered. This requires further treatments of the reservoir, including water injection with chemicals to penetrate into the oil-wet matrix by changing the wettability. In this paper, we investigate the effect of wettability alteration of the oil-wet rock on solvent as well as oil recovery. Different wettability-alteration agents were tested, including cationic and anionic surfactants, ionic liquids, nanofluids, high-pH solutions, and low-salinity water. The potential of these materials to modify the wettability of aged sandstone and limestone samples was evaluated by use of imbibition tests. After conducting a total of 35 experiments, the most-promising wettability-alteration agents were identified to be anionic surfactants and high-pH solutions in addition to low-salinity water for the sandstone cases. Ion-pair interaction in sandstone and the dissolution of mixed-wet clay particles in carbonate are the main mechanisms of wettability alteration by those chemicals. Cationic surfactants and high-pH solutions were identified as the best wettability modifiers for the limestone samples. Although cationic surfactant changes the wettability by the ion-pair-interaction mechanism, the pH solution is believed to restore the water-wetness by decreasing the attraction forces between calcite and organic components. Ionic liquid at low concentration is able to alter the wettability of carbonate better than other conventional wettability modifiers. One important finding of this work is that solvent injection in heavy-oil-containing reservoirs is essential to condition the reservoir (i.e., to dilute the heavy oil before any wettability-alteration treatment can take place).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.000 | 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 teacher head, 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".