Low Salinity Waterflooding; A Promising Prospect to Improve Oil Recovery in the Niger Delta Oil Fields
Bibliographic record
Abstract
Low salinity waterflooding is considered one of the most promising and cost–effective methods in oil recovery as a result of wettability change from oil-wet to water-wet. This work considered reducing the residual oil saturation by injection of low salt concentration in order to improve oil recovery. The objective of the study is to reduce the residual oil saturation. In this study, Simulation has been carried out on a synthetic model by using (ECLIPSE 100) as the simulator. Different Salinities of 500ppm,1000ppm, 1500ppm, 2000ppm, 5000ppm, 7000ppm, 10000ppm,30000ppm and 40000ppm were evaluated. Low salinity water was injected at the first year of production and continues to the end of the production life.Effect of salinity on oil recovery was also evaluated. The results obtained showed that low salinity waterflooding improved oil recovery at different salinity as compared to fresh water waterflooding. In conclusion, based on the results of this work, it is possible to choose the best salinity ratio that gives the lowest residual oil saturation.
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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 | 0.000 |
| 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 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".