Experimental study of secondary and tertiary modes combined low salinity water and polymer flooding in sandstone porous media
Bibliographic record
Abstract
Abstract Combined low salinity water (LSW) and polymer (LSP) flooding is the most attractive method of enhanced oil recovery (EOR). Considerable research has investigated effective mechanisms of LSP flooding. In this study, 10 laboratory core flood tests were carried out to evaluate the effects of LSW injection into samples without any clay particles, the timing of LSW injection, and the advantages of adding polymer to the injection water for EOR. Secondary and tertiary LSW injections were performed on sandpack samples with different wettability states and water salinity. Tertiary LSW injection after secondary synthetic seawater (SSW) injection in oil‐wet samples resulted in 13% more oil recovery, while the water‐wet sample showed no effect on the oil recovery. Secondary LSW injection in oil‐wet porous media improved oil recovery by 8% of the original oil in place (OOIP) more than secondary SSW injection. Tertiary LSP flooding after secondary SSW injection in the oil‐wet sample provided a recovery of 67.3% of OOIP, while secondary LSW injection followed by tertiary LSP flooding yielded the maximum ultimate oil recovery of about 77% of OOIP. The findings showed that the positive EOR effects of LSW and LSP flooding were the results of wettability alteration, pH increase, improved mobility ratio, better sweep efficiency, and oil redistribution. In addition, results showed that wettability alteration is possible without the presence of clay particles. The findings of this study can help for a better understanding of fluid propagation through the porous media and an investigation of delays in reaching ultimate oil recovery.
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 | 0.000 |
| 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) | 0.001 | 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".