Multi-physics microstructural modelling of a carbon steel pipe failure in sour gas service
Bibliographic record
Abstract
This study presents a comprehensive failure analysis of an ASTM A106B steel pipe exposed to sour natural gas, focusing on degradation and cracking mechanisms. A range of experimental methodologies, including visual inspection, chemical spot tests, XRD analysis, SEM-EDS examination, metallographic analysis, and hardness testing, were employed to identify critical material deficiencies. The findings indicate that environmentally assisted cracking (EAC) initiated at the pipe’s outer diameter (OD) and propagated inward. The experiments also revealed a hardness gradient across the pipe’s thickness and a non-uniform distribution of microstructural inclusions. Additionally, a coupled chemo-mechano-damage finite element analysis (FEA) was conducted to simulate crack propagation driven by hydrogen embrittlement. The FEA used a phase-field approach to model interactions between hydrogen diffusion, mechanical stresses, and microstructural features such as non-uniform inclusion distribution and varying hardness across the pipe wall. The simulations successfully mimicked the crack growth path under sulphide stress cracking (SSC) conditions, demonstrating the influence of material inhomogeneity. The results confirmed that failure initiated at the OD and propagated inward due to hydrogen accumulation at inclusions. These inclusions caused higher gradients of hydrostatic stress, accelerating hydrogen accumulation and crack initiation in regions with a higher inclusion density. Regions of higher hardness were particularly susceptible to failure, as they exhibit lower fracture toughness, which is further degraded by hydrogen diffusion, accelerating the failure process. This study highlights the critical role of microstructural heterogeneities and hydrogen embrittlement in pipeline failure and suggests that the methods presented can be applied to pipelines in hydrogen blending or pure hydrogen transmission, offering key insights for improving material selection and design for pipelines in sour gas and hydrogen environments. • Conducted experimental failure analysis of ASTM A106B steel pipe exposed to sour gas. • Identified EAC initiation at the outer diameter due to hardness gradients and inclusions. • Simulated failure by integrating phase-field modeling and hydrogen diffusion. • Provided insights for pipeline integrity in sour and hydrogen environments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".