Successful Field Application of Novel, Nonsilicone Antifoam Chemistries for High-Foaming Heavy-Oil Storage Tanks in Northern Alberta
Bibliographic record
Abstract
Summary Substantial foam formation was being experienced by two operators in several heavy-oil leases in northern Alberta. This 10 to 12°API crude had a large foaming potential and a unique foam-formation mechanism. Crude oil passed from the wellhead through a short flowline to a pair of hydrostatically balanced and heated storage tanks. The storage tanks heated the crude oil from approximately 50 to 85°C. In doing so, it caused gas breakout and degassing, which resulted in the formation of a thick, persistent foam in the top of the tanks. The foam would enter the transportation trucks and end up at the local battery and create carry-over and separation problems in the process systems. Silicone antifoam products were not acceptable because of their poor environmental profile and the influence these large molecules had at the refinery where the crude oil was shipped for (predominantly) asphalt manufacture. A wide range of chemistries was tested including phosphate-based products, ethoxylated and propoxylated esters, polyethylene glycol esters and oleates, alcohols, fatty alcohols, and ethoxylated and propoxylated alcohols. One of the major challenges detailed in this paper is that all products had to be freeze protected to −40°C. This was significant because many antifreeze chemicals affect the efficacy of antifoam chemicals. This paper details evolution of testing leading to the field application of this combined defoamer/antifoam chemistry. Initial laboratory screening is included, which describes a laboratory test method designed to mimic the foam-formation environment in the field more accurately. The paper also illustrates the field-trial evaluation, as well as the case histories of full field implementation of the highest-efficacy products.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".