Successful Application of a Low Dose Surfactant Injectivity-Aid in a Canadian Polymer Flood
Bibliographic record
Abstract
Abstract To achieve a successful polymer EOR application, it is crucial to maintain high polymer injectivity below the fracture pressure. This helps to prevent fracture propagation in the reservoir, which can cause fast breakthroughs, while still enabling the fastest recovery of oil. Laboratory and field testing of the addition of surfactant into a polymer injection system has shown that it can reduce the interfacial tension between injected fluids and reservoir oil, enabling more efficient oil displacement and enhanced sweep efficiency. Additionally, tailored surfactant can modify rock wettability and mitigate polymer retention in the reservoir, thereby improving polymer injectivity. In a previous paper related to this work, He et al (2024) conducted a detailed laboratory study to select an effective low dose surfactant injectivity-aid for the Atlee Buffalo field in Canada. The study comprised of fluid characterization, thermal stability, interfacial tension (IFT), emulsion tendency, polymer compatibility, and core flood performance testing. It was concluded from the laboratory core flood experimental results that the addition of a unique surfactant, at low concentrations, did indeed enhance both injectivity and oil recovery. This paper explores the results from the field pilot application of the selected surfactant from their study into an ongoing polymer flood project. Changes to the injectivity of wells moving from waterflood to polymer flood, and again from polymer flood to polymer-surfactant flood, have been assessed. Review of the initial field pilot trial data shows a positive impact of the surfactant injectivity-aid mixed within the polymer solution that was injected as part of an EOR polymer scheme. The injection data demonstrates that even at low dosage, the addition of surfactant to polymer solutions can stabilize and improve the injection process.
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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 | 0.001 | 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 | 0.000 |
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".