Benchmarking the potential of a resistant green hydrocolloid for chemical enhanced oil recovery from sandstone reservoirs
Bibliographic record
Abstract
Abstract Polymer injection into oil reservoirs stands as a primary technique for enhanced oil recovery (EOR), employing either natural or synthetic polymers that dissolve in water. Proper performance in salinity and reservoir temperature creates a limitation to replace natural material with common chemicals and this has led researchers to try to identify new material for this application. Continuing the efforts and overcoming the challenge, this research introduces and examines a high‐performance natural polymer extracted from garden cress seeds for this application. Several experiments were planned and executed based on the existing EOR standards and literature. Comprehensive analyses and viscosity measurements were performed to identify the behaviour of solutions and the effects of concentration, shear rate, salinity, and temperature. Essential tests such as wettability and polymer adsorption were also done by contact angle measurement and flooding into a sandstone plug, respectively. The produced polymer was able to effectively maintain the viscosification properties at temperatures up to 95°C. Similarly, increasing the salinity up to 140,000 ppm did not affect its efficiency and the viscosity value remained in the useful range. The viscosity of the mature solutions at 35°C after 30 h at concentrations of 200, 400, 600, 800, 1000, and 1200 ppm was 8.61, 18.59, 31.27, 65.41, 95.38, and 149.75 mPa, respectively. At 1000 ppm and temperatures of 35, 55, 75, and 95°C, the viscosity was 95.38, 90.57, 86.73, and 84.72 mPa · s, respectively. At concentrations of 600, 800, and 1000 ppm, the wettability altered to intermediate‐wet, while at 1200 ppm, altered to water‐wet. Polymer injection caused an increase in recovery equal to 18.6%. The water cut increased with a little delay in the initial volumes of water injection at a high rate and reached its maximum. Then after the injection of 0.3 PV of polymer, there was a sharp and continuous drop until reaching 35% of the production fluid volume.
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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) | 0.001 | 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".