Guidelines to Develop Fitness-for-Service Assessments in Oil Pipelines Exposed to Corrosive and Geotechnical Environments
Bibliographic record
Abstract
Industry standards (i.e. API 1160, ASME B31.4 and B31.8S-2001, CSA-Z662-2003) and regulations (i.e. US DOT 49 Parts 195-2002 and 192-2003, and NEB On-shore 99) have delineated the risk-based elements of oil and gas pipeline integrity management programs. A Fitness-For-Service Assessment is part of an overall Integrity Management Program that is implemented for the pipeline system depending on the required pipeline operational conditions, severity of integrity threats, and their impact or consequences to the public, environment and employees. This paper provides guidelines for pipeline operators of oil pipeline systems exposed to corrosive and geotechnical sensitive environments and high consequence areas to develop long term integrity plans. In this case, the pipeline integrity plans were prepared based on the integration of data and assessments such as metal loss, geometry and strain in-line inspections, product corrositivity, cathodic protection, geotechnical hazard identification, and pipe class location/high consequence areas. Guidelines for developing near-term integrity plans are herein provided based on best industry practices and regulations. In 2002, Oleoducto Central S.A. (Ocensa) and CC Technologies initiated the Phase 1 of the Fitness-for-Service assessment of 698 km of NPS 16/30 crude oil pipeline from Cupiagua to Coven˜as. Phase 1 was comprised of an internal corrosion study to assess the corrosivity of the product and its impact in the future. Corrosivity of the crude oil was determined from laboratory testing and correlated to the pipeline operational and topographical conditions. In 2003, the Phase 2 of the Fitness-for-Service assessment was comprised of a review of the near-term maintenance program and the development of the long-term maintenance program. The long-term integrity plan program for corrosion features was developed using a deterministic and probabilistic corrosion growth modeling to determine excavation/repair and re-inspection interval alternatives. The corrosion growth modeling took into account the in-line inspection tool accuracy based on the field validation program. The most cost effective alternative was identified by using a cost benefit analysis technique. This implemented approach contributed to timely schedule maintenance activities. In addition, the selected excavations confirmed with high confidence the results from the Ocensa-CC Technologies Canada predictability model. Geometry features reported by the geometry/inertial in-line inspection were initially evaluated, and correlated to the corrosion in-line inspection data, and the geotechnical hazard study to identify potential locations of slope instability, river-crossing scouring for assessing internal corrosion criticality. Strain areas were also assessed and correlated to pipe wall deformation and potential areas of land movement. Pipe class location limits were determined based on latest dwelling locations and distribution, and then correlated to the reported corrosion features for verifying minimum safety factors. The long-term maintenance program was integrated from all assessments performed on the identified integrity threats. As a result, guidelines were prepared for implementing technically sound and economically-optimized long-term inline inspection, excavation and repair plans.
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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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.011 |
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".