Inhibitor Dosage Rates and Corrosion - A CFD Model Investigating Inhibitor Over-Dosing and Increased Corrosion Rates in Subsea Pipelines
Bibliographic record
Abstract
Abstract Injection of corrosion inhibitors is employed to decrease internal corrosion rates in subsea carbon steel pipelines. The corrosion inhibitor availability model is now well established, however, the real-time availability of inhibitors is affected by different factors not addressed by the model. For instance, the morphology of corrosion product scales present would have either negative or positive effects on corrosion rates, but are not captured in the inhibitor availability model. In order to conclusively investigate this and other factors affecting inhibitor availability, the mechanistic details of the inner workings of inhibitors, especially with regards to three-phase flow subsea systems need to be well understood. The effects of the characteristic persistence of inhibitors in multiphase flow remain unclear. Although the impact of these knowledge gaps is widely perceived to be limited with respect to the inhibition of bottom of line corrosion, whilst being critical only for probable and effective inhibition of top of line corrosion, the reported ‘inhibited’ corrosion rates of subsea pipelines tell a different story. In addition, the mechanisms involved in inhibitor over-dosing leading to increased flow induced localised corrosion rates remain a mystery. It has been observed that at higher than ‘optimal’ inhibitor dosage rates, the CO2 corrosion rates of carbon steel lines are increased. The increased corrosion rate is commonly attributed to the interaction between incident local wall shear stresses (high droplets concentration, micro-turbulences, etc) and the characteristic inhomogeneous protective scales formed at high inhibitor concentrations. This observation is contrary to the industry perception of inhibitor under-dosing and its effect on inhibitor availability being the only concern in terms of inhibitor dosage rates; and brings into sharp focus the necessity to control inhibitor dosage rates to avoid both under-dosing and over-dosing. This paper therefore aims to investigate these phenomena using Computational Fluid Dynamic (CFD) simulations. Further development of a new technique suggested between inhibitor concentration and the inherent integrity of protective scales formed will be proposed in the study.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".