Associative Polymers Outperform Regular Polymers Displacing Heavy Oil in Heterogeneous Systems
Bibliographic record
Abstract
Abstract The use of polymer floods to efficiently displace heavy oils with viscosities up to 10,000 mPa.s has be tested successfully in laboratory scale evaluations; commercial success on the field scale has been achieved with oil viscosities up to 2000 mPa.s in Western Canada1 and other parts of the world1. Since it has been established that the polymer flood technology can be successful at displacing heavy oils on a field scale, it is timely to improve the efficiency of this technology. Researchers have shown that heterogeneities are more detrimental when waterflooding heavy oils than experience obtained from conventional waterfloods3, 4. Hence, it is essential to understand the polymer flood displacement of heavy oil in the presence of heterogeneities. In addition, it was beneficial to conform the impact of large scale heterogeneities with judicious use of associative polymers. Building a high and low permeability layer into a cylindrical sandpack allowed for demonstrating the impact of heterogeneities on a waterflood and polymer flood displacing heavy oils; the high permeability layer had a permeability 10 times greater than the low permeability layer. The reduced oil recovery in the heterogeneous, dual permeability core can be modeled correctly using a reservoir simulator if a capillary pressure difference curve is introduced during the simulations. The capillary pressure difference curve controls the degree of cross-flow from the high permeability layer to the low permeability layer and corrects the sweep efficiency. Salinity and hardness tolerant associative polymers suitable for injection into reservoir core have been screened and developed for heavy oil displacement processes. These specialty polymers generate a higher in situ apparent viscosity by forming large hydrodynamic radii through association between polymer molecules. In reservoir applications, these associative polymers may generate tremendous resistance factors in high permeability streaks. The dual permeability corefloods demonstrated that associative polymers outperformed the regular partially hydrolyzed polyacrylamides in two aspects: (1) the associating polymer generated incremental oil recovery after HPAM recovery and (2) the mobility reduction (or resistance factor) of the associative polymers was significantly higher than HPAM. Hence associative polymers can be used for blocking and diverting purposes in high permeability layers where regular polymers may not be as effective.
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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) | 0.002 | 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".