Prediction of “Classic” and “Flow-Induced” Internal Pipeline Top-of-the-Line Corrosion (TLC) Mechanisms Using ICPM: Model Prediction Confirmation and Applicability in Gas Condensate Pipeline Operations
Bibliographic record
Abstract
Abstract This paper investigates the potentially recurring and complex degradation issues where internal top-of-the-line (TOL or TLC) corrosion problems in a mature Alberta, Canada gas field have been observed over several years. As a test-case basis, an aging pipeline system (40+ years) with suspected TLC damage has been prevalent for a portion of its operational life with the problem indiscriminately appearing at various locations. The intended project deliverable was to conduct internal corrosion predictive modeling (ICPM) using a proposed and soon-to-be-released wet-gas internal corrosion direct assessment (WG-ICDA) approach for 17 pipeline regions with several hundred pipeline subregions in an attempt to confirm and unravel the nuances associated with possible TLC problems scattered throughout a large gas production and gathering system. However, the focus of this paper examines the pre-qualification test of the modeling conducted on a smaller 88.9 mm (3” nominal) pipeline which had been previously inspected via in-line inspection (ILI) but the results were not divulged until completion of the ICPM. The ICPM modeling platform utilized Broadsword’s in-house proprietary model called enpICDA™(1). It became readily apparent from the early stages of the modeling that the 88.9 mm pipeline system likely had other corrosion mechanisms occurring in the remaining pipe body which were inextricably linked by the operational dynamics of the system. Specifically, the active corrosion mechanisms are derived from the fluid hydrodynamic and mass transfer interactions of the wet gas. The bottom of the pipeline cannot be ignored in the context of TLC. It was positively determined that annual mist flow regimes exacerbate TLC "grooving" or "streaking".
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".