KOC’s Integrity Management Program for Non-Piggable Pipelines: A Case Study
Bibliographic record
Abstract
More than half of the world’s oil and gas pipelines are classified as non-piggable. Pipeline operators are becoming aware there are increased business and legislative pressures to ensure that appropriate integrity management techniques are developed, implemented and monitored for the safe and reliable operation of their pipeline asset. The Kuwait Oil Company (KOC) has an ongoing “Total Pipeline Integrity Management System (TPIMS)” program encompassing their entire pipeline network. In the development of this program it became apparent that not all existing integrity management techniques could be utilized or applied to each pipeline within the system. KOC, upon the completion of a risk assessment analysis, simply separated the pipelines into two categories consisting of piggable and non-piggable lines. The risk analysis indicated KOC’s pipeline network contains more than 200 non-piggable pipelines, representing more than 60% of their entire pipeline system. These non-piggable pipelines were to be assessed by utilizing External Corrosion Direct Assessment (ECDA) for the threat of external corrosion. Following the risk analysis, a baseline external corrosion integrity assessment was completed for each pipeline. The four-step, iterative External Corrosion Direct Assessment (ECDA) process requires the integration of data from available line histories, multiple indirect field surveys, direct examination and the subsequent post assessment of the documented results. This case study will describe the available correlation results following the four steps of the DA process for specific non-piggable lines. The results of the DA program will assist KOC in the systematic evaluation of each individual non-piggable pipeline within their system.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".