A Case History of Heavy-Oil Separation in Northern Alberta: A Singular Challenge of Demulsifier Optimization and Application
Bibliographic record
Abstract
Summary This case history tracks the continual improvement cycle for the fluid-separation process of a heavy-oil/oil-sands production facility in northern Alberta over a period of 3 years. The major challenge posed by the operator of this 13 to 16°API crude oil was to move away from injection of two separate demulsifier formulations to injection of a single product. This was not an easy task because of the very different conditions that existed at the two injection locations. The first location was at a series of injection points upstream of the gathering stations before separation where temperatures could reach subzero conditions, and the second was at the battery receiving facility where heating increased temperatures to 100°C. Water cut and shear were also very different, and the operator required a very strict 0.2% basic sediments and water (BS&W) on the crude exiting any of the four treater tanks. To complicate issues further, crude-oil viscosity ranged from 500 to 5,000 cp. A unique bottle testing method was developed and used to simulate the field conditions as accurately as possible. Details are given on the chemistry of the individual components of the demulsifier determined to be so crucial to adequate performance and how this was optimized in the field after being identified from the bottle tests. Results show how careful consideration was given to the concentration of the demulsifier bases in the blends, and show the curious observation that dilution of the final product made a big difference to the final performance in the field. Elaboration is given on potential mechanisms explaining the dilution effect, and this paper will conclude with observations on how careful design of field testing followed by field implementation can indeed solve complex separation issues and address individual well, battery, and field requirements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".