A critical review of low salinity water flooding: Mechanism, laboratory and field application
Bibliographic record
Abstract
Low-salinity water flooding (LSW) is a promising new technique for enhancing oil recovery (EOR) in both sandstone and carbonate reservoirs. The potential of LSW has drawn the attention of the oil industry in the past decade. Along with the few successful field applications of LSW, various studies in this field in recent years have been conducted mainly at the lab scale. The main objective of this critical review was to investigate the potential of this EOR technique in improving oil recovery and the mechanism under which it operates. As a result, various mechanisms have been proposed. However, no consensus on the dominant mechanism(s) in neither sandstones nor carbonate reservoirs has been reported, and the oil industry is continuing to discover the leading effects. Herein, we provide the chronicle of LSW, analysis of the proposed mechanisms of enhancing oil recovery using LSW in recent findings, some laboratory observations, and finally, some successful field applications. From this review, despite the promising potential justified by both laboratory studies and field applications, there exists a large number of unsuccessful field case studies. LSW is viewed as an immature EOR technique with many ambiguities because definitive conclusions about which mechanism(s) is responsible for improving oil recovery remains elusive and a bewilderment to the oil industry.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".