Minimizing Surfactant Adsorption Using Polyelectrolyte Based Sacrificial Agent: a Way to Optimize Surfactant Performance in Unconventional Formations
Bibliographic record
Abstract
Abstract Surfactant is a critical component in fracturing fluid to help achieve higher productivity from unconventional oil and gas formations. However, adsorption of surfactant onto reservoir rocks and proppant could lead to inefficient use. The large surface area of shale formations (up to 700 m2/g) could yield a significant loss of surfactant attributed to adsorption onto reservoir rock. Field production data from the Barnett shale indicates that most surfactant could be tied up within the reservoir. For example, 3 gpt (3 gal/1,000 gal ~3000 ppm) of surfactant was injected into the reservoir, and only 0.5 gpt (or 500 ppm) was recovered when production began; surfactant concentration further decreased to 50 ppm in one month. Strong adsorption could potentially limit the contact of surfactant with reservoirs, as most surfactants could be adsorbed near-wellbore (NWB) before reaching the interior of the reservoir. Therefore, a need exists to reduce surfactant's adsorption to further optimize its performance during hydraulic fracturing. This paper discusses static and dynamic adsorption tests performed to evaluate the use of polyelectrolytes (PETs) to reduce surfactant adsorption onto the Rainbow shales in Canada. Dynamic surface tension measurements and UV-vis data indicated surfactant adsorption could be significantly reduced using PETs based on the competing mechanisms. Additionally, oil recovery laboratory results reveal that surfactants extract more oil in the presence of PETs. On the other hand, scale inhibition performance testing suggests that these PETs can also serve as scale inhibitors. In this study, the inhibition efficiency of the PETs is compared to commonly used phosphonate and polymeric scale inhibitors. Static bottle testing results indicate the PETs can effectively control calcium carbonate scale formation. The synergistic effect between surfactants and PETs suggests a new route for formulating multifunctional surfactant blends that can be tailored to specific formation rocks.
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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) | 0.000 | 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".