Insights into Heavy Oil Recovery by Surfactant, Polymer and ASP Flooding
Bibliographic record
Abstract
Abstract At the end of primary production in heavy oil reservoirs, significant volumes of continuous oil remain in place. As production rates decline this EOR target has tremendous value for heavy oil producers. Many of these reservoirs are poor candidates for thermal recovery. Furthermore in regional sands or post-CHOPS systems, it may not be easy to pressurize these reservoirs for solvent-based recovery. Chemical flooding has potential for EOR in these systems, because the injection of chemicals can lead to the buildup of pressure gradients between injectors and producers, at least at the laboratory scale. These pressure gradients evolve due to improved viscosity of polymer solutions, the formation of emulsions in surfactant or AS floods, or both. The objective of this work is to improve our understanding of the mechanisms by which heavy oils are produced through chemical flooding. Linear core floods were run on systems containing two heavy oils of variable viscosity: 500 mPa•s and 16,000 mPa•s. For the lower viscosity oil polymer floods and ASP floods are compared. These tests illustrate the impact of improving the injection fluid viscosity vs. the additional benefit from the addition of surfactant. It was observed that heavy oil is produced more efficiently from ASP flooding compared to polymer flooding alone. The residual oil saturations are lower in ASP floods, even with lower differential pressure across the core. For the higher viscosity oil some production was achieved through AS flooding alone, but the addition of polymer was important for improving recovery. Tests were also run on a parallel core system, containing cores of relatively high and low permeability. This was a representation of a post-CHOPS reservoir containing preferential flow channels due to the presence of wormholes. Both surfactant and ASP solutions only accessed the high permeability core, so oil was bypassed in the lower permeability sand even with the addition of chemicals to water. This result demonstrates that laboratory studies may be dramatically over- estimating the success of chemical flooding in heavy oil, and poses a challenge for successful implementation of chemical floods in heterogeneous post-CHOPS heavy oil fields.
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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 | 0.001 |
| 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".