Laboratory and Simulation Study of Optimized Water Additives for Improved Heavy Oil Recovery
Bibliographic record
Abstract
Abstract This paper discusses a laboratory evaluation of the feasibility of different chemical flooding strategies and a simulation study to optimize the feasible strategies for a west-central Saskatchewan heavy oil reservoir. The integrated experimental approach was composed of oil/brine interfacial tension (IFT) measurements, polymer viscosity measurements, wetting tendency measurements, and sandpack coreflood tests. The experimental results showed that the equilibrium interfacial tension between reservoir oil and formation brine could be lowered to an ultralow level (0.05 mN/m) by adding a certain concentration of alkali and surfactant into the brine. The addition of alkali and surfactant caused the wettability characteristics in all tested systems to become oil-wet. All of the polymer solutions exhibited pseudo-plastic behaviour, i.e., the apparent viscosity decreased with increasing shear rate. A series of sandpack coreflood tests were carried out to investigate the recovery performance of alkali + surfactant, polymer, and alkali + surfactant + polymer (ASP) floods. Enhanced oil recoveries (from the chemical flood and extended waterflood) varied significantly from 0.71 to 14.65% OOIP. The coreflood results suggest that in enhanced waterflooding for recovering viscous heavy oil, mobility control by polymer is more important than IFT reduction by alkaline/surfactant. In the simulation study, the relative permeability curves were obtained through history matching. Then, as the sensitive operating parameters, the ASP slugs and polymer concentrations were tuned to show their effects on enhanced heavy oil recovery (EHOR). In summary, ASP flooding provides synergistic effects that can maximize the recovery performance.
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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) | 0.000 | 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 | 0.001 | 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".