Evaluation of Low Saline “Smart Water” Enhanced Oil Recovery in Light Oil Reservoirs
Bibliographic record
Abstract
With the depletion of natural driving forces responsible for pushing the oil from reservoirs & declination of oil recovery after secondary stage, the emphasis is now on EOR techniques. The low saline flooding is a type of EOR which gains the attention of researchers due to its easiness to use implications, less cost & environment-friendly nature. Though the low salinity effect has been seen in various labscale core flooding experiments as well as field pilot projects, the mechanism which actually leads to this enhancement in recovery is still the area of research among researchers which is wide open and needs to gain consensus. Seeing the wide mechanisms taking place under different scenarios, it is certain that more than one mechanism is actually supplementing each other in reducing the residual oil saturation while LSW flooding. This study has been undertaken investigations on the low saline flooding in unconsolidated Ottawa sandpack cores with two different, Weyburn & Pelican crude oil, to find out the optimum salinity, LSW Slug Size & underlying mechanisms during LSW flooding. Several core flooding experiments were performed under secondary as well as tertiary recovery stage by unsteady state method. With the reduction of brine salinity from 5000 PPM to 1500 PPM, the oil recovery increased in secondary stage & decreased further upon reduction in salinity to 500 PPM gaining the peak at 1500 PPM. Small enhancement in tertiary recovery of 2.24% observed upon switching to 1500 NaCl PPM brine after injection of formation brine in secondary stage for Weyburn Oil, though large tertiary recoveries of the order of 9.95% for effective oil viscosity of 4 cP and 7.32% for 29.7 cP were observed for n-dodecane diluted Pelican Oil. LSW slug size of 25% pore volume was found to be effective in producing Weyburn Oil in secondary stage.
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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".