Fast-acting Post Hydrotest Batch Chemical Treatment for Large Pipelines
Bibliographic record
Abstract
Abstract Newly constructed long-distance pipelines are often hydrotested with water from a natural reservoir without the use of chemical treatment. Chemicals are typically not used for various reasons: (i) the water is usually released back into the natural environment after use, (ii) the environmental impact of a chemically treated water release during hydrotesting is higher than an untreated water release, and (iii) the volume of water used to hydrotest long distance pipelines are too large for economical chemical treatment. It could take months before the hydrotested pipelines are put into service leading to potentially severe internal corrosion risk due to residual water and associated corrosion factors such as oxygen, salts, solids and microbes. Such risk can be reduced by conducting a batch chemical treatment with corrosion inhibitors and biocide. There are several key challenges for batch treatment of corrosion inhibitors: (i) low contact time during the batching process (e.g., 10 seconds) requiring fast acting and persistent inhibitors, (ii) vapor phase inhibition in situations when batched inhibitor does not wet the entire pipe surface, (iii) impact of brine chemistry, (iv) maintain inhibition at low concentrations for situations where batch pill is diluted as it picks up stagnant water in the pipe, (v) impact of inhibitors on refinery operations when residual inhibitor is carried with crude oil and (vi) HSE risks associated with the presence of residual inhibitor on the pipe wall and vapor spaces. This HSE risk is an especially difficult challenge to resolve. Here we report results of a laboratory study to investigate inhibitors to address these challenges. Corrosion in fluid, headspace and interface was measured by suspending coupons. Biocide compatibility was studied by ATP tests. Impact of inhibitor crude oil carryover on refinery processes was conducted with a battery of harms tests.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".