Hydrophobically-Modified Cellulosic Polymers for Heavy Oil Displacement in Saline Conditions
Bibliographic record
Abstract
Abstract Commercial polymer floods have generally used partially hydrolyzed Polyacrylamide (HPAM) as the polymer of choice, since HPAM generates high viscosities in relatively fresh brines at a reasonable cost. In high salinity or hardness brines, the anionic charges on the HPAM polymer chain interact with the dissolved cations, shrinking the hydrodynamic size of the polymer molecules. The smaller hydrodynamic radius generates lower viscosities and requires higher polymer concentrations. In order to reduce the polymer loading in saline brines, a series of hydrophobically modified biopolymers have been synthesized. The non-ionic nature of hydroxyethyl cellulose (HEC) provides an ideal polymer back bone since it does not interact with the dissolve cations. Hydrophobic groups (aliphatic hydrocarbon chains) are attached to the backbone of the polymer to impart associating properties between polymer molecules. The inter-molecular interaction between the hydrophobic moieties led to a significant viscosity increase in comparison to polymers without hydrophobic groups. Several screening steps were used throughout the product development to arrive at practical polymer formulations: 1) Filtration; 2) Sandpack tests; 3) Corefloods in the presence of oil. By varying molecular weight of the base HEC polymer and the amount of hydrophobic content, practical polymer products were developed for polymer flood applications. Coreflood tests showed that the hydrophobically modified HEC generated significant resistance factor in saline brines, outperforming the HPAM polymers.
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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 | 0.001 |
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".