Impact of Acid Additives on the Rheological Properties of a Viscoelastic Surfactant and Their Influence on Field Application
Bibliographic record
Abstract
Summary This paper examines a new class of viscoelastic surfactants (amphoteric) that are used to enhance sweep efficiency during matrix acid treatments. It appears that surfactant molecules align themselves and form rod-shaped micelles once the acid is spent. These micelles might cause the viscosity to significantly increase, and induce viscoelastic properties to the spent acid. The enhancement in these properties depends on the micelle shape and magnitude of entanglement. The effects of acid additives and contaminants [mainly iron (III)] on the rheological properties of these systems were examined over a wide range of parameters. Viscosity measurements were conducted using specially designed viscometers to handle very corrosive fluids. Measurements were made between 25 and 100°C, and at 300 psi at various shear rates from 58 to 1,740 s−1. Acid additives included corrosion inhibitors, inhibitor aids, an iron control agent, a hydrogen sulfide scavenger, an anti-sludge agent, and a nonionic surfactant. Effects of mutual solvents and methanol on the apparent viscosity were also investigated. It is observed that temperature, pH, shear conditions, and acid additives have a profound influence on the apparent viscosity of the surfactant-acid system. The viscosity and related properties are very different from what were observed with both natural and synthetic polymers. The differences in these properties were characterized and correlated with the type and nature of the additives used. Optimum conditions for better fluid performance in the field were derived.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".