Low Dosage Novel Surfactant Technology for Improving Polymer Injectivity in Atlee Buffalo Oil Pools
Bibliographic record
Abstract
Abstract For a successful polymer EOR application, maintaining high polymer injectivity below fracture pressure is important to prevent fracture propagation in the reservoir causing fast breakthrough. Adding surfactant into the polymer system can facilitate the reduction of 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, and thereby improve polymer injectivity and well production. In this work, a detailed surfactant selection workflow and key strategies are presented to evaluate the efficiency of surfactants in improving polymer injectivity for the Atlee Buffalo oil pools in Canada. Laboratory analyses encompassed fluid characterization, interfacial tension (IFT), emulsion tendency, and polymer compatibility. These preliminary tests enabled the pre-screening of surfactants and the optimization of surfactant dosages. To further validate surfactant performance, core flood (CF) experiments were conducted using sandstone cores that closely resembled the reservoir's permeability and mineralogy. The CF employed a meticulously designed injection scheme to evaluate the impact of surfactants on improving polymer injectivity. Given the low dosage application strategy, it is imperative to choose a surfactant that can consistently maintain a low IFT at low surfactant concentrations. The preliminary IFT scans revealed that novel surfactant could maintain IFT from 0.15 to 0.43 mN/m for different pool oils at low concentrations. Furthermore, additional testing has confirmed that the surfactant does not induce any tight emulsions, thereby mitigating surface-related complications during production. Viscosity measurements have indicated that the surfactant has no discernible impact on the fluid viscosity influenced by the polymer, demonstrating the compatibility between the polymer and surfactant solution. Significantly, in the multiphase CF tests, it was evident that the selected surfactant led to an approximate 2.8 to 4.2 times enhancement in injectivity during polymer flooding. Moreover, under the protective influence of surfactant, injectivity loss was four times slower during polymer flooding when oil saturation dropped below 30%. Furthermore, by combining the low-dosage surfactant with polymer, an additional 15.3% oil recovery was observed after polymer injection alone. These results underscore the substantial benefits of adding novel surfactant in improving injectivity and overall oil recovery. In contrast to conventional surfactant-polymer flooding, this study explores a strategy of applying a low dosage surfactant to enhance polymer injectivity. By properly tailoring the surfactant, this approach harnesses the synergy between surfactant and polymer to address the challenges posed by high viscosity oil reservoir. Notably, after conducting a field trial in an Atlee Buffalo oil pool with the recommended low dosage surfactant, injection data demonstrates that it can stabilize and improve the polymer 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".