Prevention of Acid Induced Asphaltene Precipitation: A Comparison of Anionic vs. Cationic Surfactants
Bibliographic record
Abstract
Abstract With the recent proliferation of horizontal drilling specifically targeting oil bearing reservoirs, high strength acid fracturing treatments in the Beaverhill Lake formation in northern Alberta have dramatically increased in both product volume and number of treatments. The Beaverhill Lake formation is a limestone/calcareous shale that produces a desirable mid to high API sweet crude oil. Although the crude oil typically has a low concentration of asphaltenes, the oil is very sensitive to acid and/or iron induced asphaltene precipitation. As the acid strength increases and ferric iron is dissolved into solution, it becomes increasingly difficult to chemically prevent the asphaltenes from precipitating. Acid blends designed to prevent asphaltene precipitation also tend to be very emulsifying with the crude oil, therefore a careful balance between anti-sludge additives and non-emulsifiers must be found. This paper will describe the chemistry of surfactants that can be used to prevent asphaltene precipitation as a result of acid/oil contact. Specifically, a comparison of anionic versus cationic surfactants will be given, describing both the benefits and detriments of using these in acid blends. A discussion of the change from vertical well completions in the Beaverhill Lake formation to horizontal multi-zone completions will be presented. As a result of this change in completions, the desired properties of the acid blends have changed noteably. The final results of a comprehensive laboratory study to optimize cost and performance of the acid blends will be presented. A review of field case studies comparing formation response to anionic and cationic acid blends will also be presented.
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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) | 0.002 | 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".