Understanding and Modeling Corrosion in CO2 Transportation Pipelines:Findings from the Carbon Adapt Project
Bibliographic record
Abstract
Corrosion in pipelines has a huge economic impact on the pipeline infrastructure for CO2 injection. Corrosion can reduce the wall thickness of pipelines, leading them to crack and corrode, causing uncontrolled leaks or damage to the entire pipeline system. The transportation pipelines and injection wells play a key role, as they serve as the connector between carbon capture and its utilization and storage (CCUS). The overarching goal of the Carbon Adapt project is to assess the impact of CO2 injection on current oil and gas infrastructure or the installation of new ones for CO2 applications. This project aims to cultivate detailed knowledge of the interaction between CO2 (liquid or supercritical), impurities (e.g., water, H2S, NOx), process conditions, reservoir fluids, and the pipeline material required, all within the context of CCUS in the Danish North Sea. The study will measure the corrosion effect of a liquid or supercritical CO2 in the presence of impurities and provide a mathematical model to predict corrosion on existing and new infrastructure. The investigation of the CO2 impurity corrosion effect will provide fundamental and necessary information to frame future quality guidance and legislation for CO2 injection, which is currently lacking. The study aims to discern between CO2 and H2S dominant corrosion processes, presenting polarization curves for CO2 dominance and H2S concentration profiles relative to the distance from the steel surface. Importantly, this study, in its latter stages, will try to validate corrosion conditions from the flow-loop testing facility, allowing to draw comparison with experimental conditions and testing the accuracy of the developed model. The goal is to allow user customization by enabling the inclusion of user-defined reactions or the exclusion of undesired reactions, thereby offering flexibility in the corrosion calculation process.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".