Wettability State and Phase Distributions During Steam Injection with and without Chemical Additives: An Experimental Analysis Using Visual Micromodels
Bibliographic record
Abstract
Summary Phase change plays an essential role in wettability during steam injection, and oil becomes the wetting phase in the steam zone. This study investigates this unfavorable phenomenon using visual data obtained from micromodel experiments and how the wettability can be reversed using chemicals. All measurements were conducted at temperatures up to 200°C on glass-bead micromodels. The models were initially saturated with brine solution and then displaced by two types of mineral oils (450 and 111,600 cp at 25°C). Steam was then constantly injected into the micromodels to evaluate the effect of phase change and wettability status on residual saturation development. Next, chemical additives, screened from the previous contact-angle and thermal-stability measurements, were added to the steam to observe their ability in modifying phase distribution and wettability state. The results showed that phase distribution and residual oil saturation are critically sensitive to the steam phase. At any circumstances, wettability alteration with chemicals was possible. The shape and characteristics of the trapped oil with and without chemicals were identified through micromodel images, and suggestions were made as to the conditions (pressure, temperature, and time to apply during the injection application) at which these chemicals show optimal performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".