Elemental Sulfur Uptake and Corrosion Protection in Sour Gas Systems
Bibliographic record
Abstract
Abstract The presence of elemental sulfur and polysulfide severely aggravates the corrosion in sour gas systems. Application of sulfur solvent and corrosion inhibitor aims to mitigate corrosion. However, in some cases, very sticky solid deposits in pipeline are found, which is believed to be the mixture of elemental sulfur and extracted corrosion inhibitor bases. How to keep the high efficiency of chemicals in field condition and avoid the sludge formation is quite challenging. Combined products of sulfur solvent and corrosion inhibitor are developed in this work, which can be mainly used in pipeline batch program or applied by continuous injection in some situations (dry gas with turbulent flow). The evaluation of products shows that the products are tolerant to hydrocarbon dilution and has a good sulfur uptake even at low temperatures. The stability of the product upon the hydrocarbon dilution reduces the chances of active ingredient drop-out and helps the elimination of sludge formation. Autoclave tests has been conducted done for batch application using brine and the polysulfide solution under sour conditions, showing that the products provide more than 90% corrosion protection in polysulfide solution. After dissolving elemental sulfur, the products also give excellent corrosion mitigation.
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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) | 0.001 | 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".