Interfacial Contamination Between Batches of Crude Oil Due to Dead-Legs in Pump Station Piping
Bibliographic record
Abstract
Some oil pump station design layouts may contain multiple dead-legs. During the transportation of heavy crude through the pump station, these dead-legs will be filled with this crude. When a light crude batch is introduced next into the pipeline, following the heavy crude ahead, two phenomena will occur. First, contamination between batches at the interface of the two crudes will occur due to axial turbulent diffusion along the length of the pipeline itself. Second, as the light crude flows through the pump station and passes by each dead-leg containing still heavy crude from the preceding batch, the heavy crude trapped in these dead-legs will start to drain out into the passing light crude in the main run. This causes further contamination and spreading of the mixing zone between the two batches. These two different sources of contamination are addressed in this paper with the objective of accurately quantifying the extent of the contamination, with particular emphasis on the second phenomenon which could cause appreciable contamination particularly for large size and number of these dead-legs. A computational fluid dynamics (CFD) model has been developed to quantify the drainage rate of the contaminating crude into the main stream and its impact on widening the mixed zone (contamination spread) between the two batches. Two drainage mechanisms of the heavy crude in the dead-legs into the main stream of the light crude have been identified and quantified. The initial phase is a gravity-current-induced outflow of the initially stagnant fluid in the dead-leg, followed by a subsequent draining mechanism primarily induced by turbulent mixing and diffusion at the mouth of the dead-leg penetrating slightly into the dead-leg. It was found that the second mechanism takes a much longer time to drain the first, and that the break point in time where drainage switches from a predominantly gravity current to a turbulent diffusion appears to be at a specific time normalized with respect to the length of the dead-leg and the gravity current speed. The results show a consistent trend with actual interface contamination data obtained from the Keystone 2982 km pipeline from Hardisty (Canada) to the Patoka Terminal (U.S.A.).
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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 | 0.001 | 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".