An improved analytical model for low-salinity waterflooding
Bibliographic record
Abstract
Low-salinity waterflooding (LSW) is popular in the oil industry worldwide due to its improved enhanced oil recovery performance, simple operation and environmental protection. However, the mechanisms underlying LSW are still being debated. To construct an analytical model which can interpret the LSW mechanism has been a big challenge. In this paper, we will combine several mechanisms to construct an analytical model and obtain a solution for LSW. After excellent validation with the experimental data, we propose several conclusions based on our model: (1) LSW can increase oil recovery and slow down the breakthrough of water at the beginning of field development and after conventional waterflooding ; (2) the injection velocity of low-salinity water should be controlled within the proposed range in order for the clay cake to form. For a reservoir with a larger porosity, it is easier to operate the injection velocity; (3) lower salinity will lead to a higher water recovery factor due to the reduction of residual oil saturation; (4) the water saturation of the oil bank will become lower with a higher formation damage factor.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".