Degradation Behavior of High-Phosphorus Ni-P Coating for Application in Oil and Gas Industry
Bibliographic record
Abstract
Carbon steel casing and tubing, utilized for oil and gas production, may potentially experience severe corrosion due to the presence of corrosive gases (e.g., CO2 and H2S) and chlorine compounds in the production wells. To mitigate the corrosion of carbon steel, the Ni-P coating has been applied to the carbon steel surface using electroless deposition method. The extensive studies have suggested that Ni-P coating can effectively protect the substrate through isolating the substrate from the corrosive environment, and exhibits good corrosion resistance in the environment containing brine, acid, CO2 or even H2S. However, some internal microdefects derived from the deposition process as well as some external defects originated from the mechanical effect during the production process will inevitably be present in the coating. These defects are likely to pose great risks to the reliability of the coating and negatively affect its durability in the corrosive environments, especially in the coexistence of CO2 and Cl-. In this work, the degradation behavior of a high-phosphorus Ni-P coating with microdefects or an artificial defect in CO2/Cl- environments was systematically investigated using electrochemical methods and surface characterizations. The results show that although the corrosion occurs at the microdefects and extends towards the inside of Ni-P coating, the coating has a good resistance to corrosion disbonding in the CO2/Cl- environment, even with an artificial defect in the coating. Under the cathodic polarization condition (to accelerate the corrosion process), the defects in the coating provide effective pathways for the electrolyte to transport through the coating and along the coating/substrate interface laterally from the defects, thereby, causing the localized corrosion and disbonding of the coating. Finally, a corrosion model is proposed to well interpret the degradation process of the coating with microdefects in CO2/Cl- environment. The electrolyte penetrates into the micropores and causes the corrosion of coating at the micropores, promoting the initiation of the pits. As the corrosion proceeds, the accumulation of corrosive species in the pits increases the localized corrosion rate. After the pits penetrate through the entire coating, the corrosion process is governed by the substrate dissolution and the mass diffusion between the substrate interface and the electrolyte. The corrosion of the substrate propagates along the coating/substrate interface laterally and towards the depth direction due to the accumulation of the electrolyte at the exposed substrate surface, which causes local corrosion disbonding of the coating.
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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 | 0.001 | 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".