Investigating the effect of transient flow behavior from HSW to LSW on oil recovery in low-salinity water flooding simulation
Bibliographic record
Abstract
Low-salinity water (LSW) flooding is one of the newest EOR techniques which has more advantageous over other EOR techniques. This research employed a heterogeneous synthetic three-dimensional reservoir to model LSW flooding for a two-phase system including brine (high salinity to low salinity) and oil. The obtained results show that exact determination of salinity threshold and its wettability alteration coefficients are very important since they affect the maximum value of oil recovery. The oil recovery has been varied between 55.79 and 60.34% for a given injection brine salinity (500 ppm) at different salinity threshold values. Furthermore, the result reveals that aging time has a low effect on oil recovery which is around 0.066% after more than 12 years of injection. However, the fine-grid 1D simulation of a small sample demonstrates that the aging time effect should be considered in small-scale models. Furthermore, we prove that there is an optimum value of injection brine salinity for each reservoir according to its salinity threshold. Highest recovery changes occur at the salinity of 5000, 3400 and 1200 ppm for three different salinity thresholds approximately. This paper demonstrates that before any implementation of LSW flooding, many laboratory tests must be done at reservoir condition to precisely detect wettability alteration coefficient, the best injection brine salinity and flow behavior from high-salinity water to LSW.
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 | 0.000 | 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".