Critical Assessment of the Hybrid Impact of Surfactants on Modified Salinity Water Flooding
Bibliographic record
Abstract
Abstract In recent years, interest in water-based EOR methods and the combination of modified seawater with chemicals has grown due to their economic improvement of oil production. The hybrid application of low salinity water with surfactants (LSS) flooding has a promising potential to significantly increase the oil recovery. LSS flooding, a novel hybrid EOR approach, has recently proven its capability of altering rock surface wettability and reducing oil-water IFT. In this study, we present a comprehensive assessment of the fundamentals and recent developments of LSS flooding, as well as lessons learned from previous studies and the key uncertainties associated with successful implementation. The study begins with an overview of surfactant flooding, low salinity water (LSW) flooding and the hybrid application of LSW flooding processes. The LSS flooding process in different reservoir types and the recovery mechanisms associated is then discussed. The recent laboratory studies for LSS flooding and the surfactant losses associated in porous media are also reviewed. Recent studies of LSS flooding have concluded the advantages of higher oil recovery, higher surfactant stability, lower surfactant retention, and lower chemical consumption compared to conventional surfactant flooding. Most of the LSS flooding application has been performed on sandstones with remarkable outcomes, meanwhile, it’s application in carbonates has garnered attention in recent years and some promising findings were reported. The efforts of this work can provide further understanding of the LSS flooding process and its underlying mechanisms, especially in carbonates which are not fully covered in the literature. Finally, this paper gives more insight into the potential success of LSS flooding over surfactant and LSW flooding processes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".