New Insights on the Impact of Mass to Volume and Volume to Volume as it Relates to Barium Sulphate Dissolver Performance
Bibliographic record
Abstract
Abstract Dissolver technology has been developed and applied with varying degrees of success over the past few years to clean carbonate and the more challenging sulphate/sulphide scales from production tubing and process equipment. A common question that is often raised but which has no published experimental work to provide an answer is "under what liquid to solid ratio would a sulphate dissolver not be effective to clean production tubing?" A set of experiments were conducted at a range of temperatures to review the impact of surface area/mass to fluid volume ratio for barium sulphate scale. The surface area/mass to fluid volume ratio were measured from 13 examples of scaled or partially scaled production tubing in an attempt to understand why in some laboratory tests dissolvers used to remove sulphate scale appear to show better performance than is observed in the field. This paper shows that simple dissolver tests, when performed with high volume/volume ratio (1 part scale to 45 chemical) which equates to a mass to liquid volume ratio of 1:10 which is most commonly used in screening studies, can give misleadingly high performance information as they show enhanced dissolution rates and mass of scale dissolved. In fact, from the 13 scaled pipe sections examined in this work, a much higher ratio of solid mass to liquid volume (10:1 to 1:1) (equating to a volume/volume ratio of 1:4.5 to 2:1) is more typical and with these ratios dissolver performance is greatly reduced. The results from these experiments are used to give a guide as to the measured liquid to solid ratio where sulphate dissolvers have the potential to work for barium and calcium sulphate scales and to where mechanical removal of the deposit would be the better treatment option.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".