Low Carbon Emission Polymer Flooding in Heavy Oil Reservoirs: Mechanisms Learnings from Typical Field Tests
Bibliographic record
Abstract
Summary More than 70% of the global reserve were in heavy oil reservoirs. The huge energy intensity and CO2 emission makes current thermal production of heavy oil less attractive in the era of carbon neutrality. The comparison of polymer flooding and steam flooding showed that while steam injection can achieve much higher recovery than polymer flooding, polymer flooding has wider application scenarios, lower operating costs and less capital input. Various studies have shown that polymer flooding can get an additional oil recovery of up to 20% original oil in place (OOIP). Commercial application of polymer flooding in Pelican Lake (Canada), Shengli and Bohai has shown the benefit of increasing oil production and reducing water injection and production, which in turn reduced the CO2 emission significantly. The low carbon emission nature of polymer flooding was recently verified by various studies. However, the mechanisms of polymer flooding were less understood for heavy oil reservoirs due to various reasons. First, the polymer flooding mechanism regarding viscoelasticity effect in lab and reservoir was still in development. The classic capillary number theory was noticed great drawback because of invalid assumptions ( Guo et al, 2021 ,Transport in Porous Media). Some mechanisms were misunderstood. Second, the high mobility contrast between heavy oil and water and/or polymers makes the effective sweep at different places difficult. The problem of many EOR ideas such as chemical viscosity reducers and even hot steam was how to contact viscous oil in reservoirs. Finally, the reservoir complexity makes polymer flooding more difficult to get desired benefit at least due to the crossflow between layers. Some good laboratory tests can give to erroneous results in actual oilfields due to the scale differences (pore scale, core scale and reservoir scale) which were one of the most challenging parts in upscaling. Various polymer flooding field tests were reviewed and one polymer flooding application Gucheng in China was discussed. Gucheng polymer flooding has shown the limited contribution of high concentration high molecular weight viscoelastic polymer on oil recovery. By contrast, the low viscosity polymer solution worked very well in heavy oil reservoirs (Canada, USA). One important finding was the mild viscosity can get balance between productivity and mobility control. Polymer flooding in heavy oil reservoirs was different from in conventional reservoirs. As long as water flooding works, polymer flooding works better in heavy oil reservoirs.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".