Long Distance, Long Duration Case Study of Successful Inhibitor Application on a Very Light Hydrocarbon Pipeline
Bibliographic record
Abstract
Abstract The operating mileage of North American pipelines transporting lowdensity, high APIgravity hydrocarbon liquids has increased significantly over the past two decades. This trend began in the early 2000’s because of increasing oil sands production in Canada, where light hydrocarbons are used to reduce the viscosity of bitumen to achieve practical ‘heavy oil’ specifications suitable for pipeline transport. The light tight oil boom of the 2010’s continued this trend, as multistage fracking unlocked high API gravity liquids from previously low permeability shale and sandstone formations. While different in origin and composition, light tight oils (LTOs), diluents used for bitumen transportation, and some refined products are similar in terms of density (density 680-800 kg/m3, or API 35-70), viscosity (<1cSt), and steel surface wetting behavior (hydrophilic). In previous works, the use of the NACE TM0172 ‘spindle test’ was demonstrated as an effective screening tool to assess the performance of corrosion inhibitors1 and led to the recommendation of hydrocarbon soluble inhibitors. Digital coupon analysis was used to provide unbiased quantification of results allowing better discrimination of inhibitor performance.2 The authors also provided an exploration of parasitic inhibitor consumption by fluid borne particulate.3 This work completes a decade-long pipeline case history where effective corrosion inhibition was used to solve a myriad of particulate related issues in a long-distance diluent carrying pipeline. Significant learnings about the performance and longevity of inhibitor (successfully applied at only one location of a 2500 km pipeline), the measurement of dissolved and particulate adsorbed inhibitor, the effect of maintenance pigging on the inhibitor film, and unexpected learnings about the ability of the inhibitor to disperse accumulated particulate is presented.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".