Bibliographic record
Abstract
Abstract This paper highlights the importance of primary and supplemental leak detection system selection for arctic and sub-arctic offshore pipeline projects. An overview of the more viable leak detection technologies is summarized along with a brief historical summary of the leak detection systems that have been installed on three existing offshore arctic pipeline projects. Finally, potential fiber optic cable technologies are reviewed in terms of the testing performed to date with recommendations for further testing to demonstrate the capabilities of these technologies for reliable use as primary or supplemental leak detection systems. Opportunities for development to extend fiber optic cable systems are also explained. It is desirable to be able to detect all potential leak sizes for an offshore arctic pipeline project. Selecting the most appropriate primary leak detection systems or a combination of a primary and secondary leak detection system for single phase oil pipelines and multiphase (oil, gas, and water) pipelines can limit the volume of oil released from a potential leak. Coverage of single phase gas pipelines can reduce emissions, reduce potential fire hazards, and, for projects having sour gas, reduce the potential consequences posed by H2S. Rapid detection of large leaks in arctic and sub-arctic locations is as important as rapid leak detection in other areas of the world. However, in arctic and sub-arctic offshore locations, the maximum potential leak volume may result from small chronic leaks that fall below the minimum achievable leak detection threshold limit that is relatively free of false-alarms. With ice cover freeze-up beginning in mid-October, break-up occurring in late June, and with no ice free areas visible for 6 months between early December and early June in the Beaufort Sea, for example, a chronic leak can develop into a large volume oil spill while shielded from view by winter arctic conditions. Sub-arctic regions, such as the North Caspian Sea and the Sea of Okhotsk (Northeastern Sakhalin Island), have similar long duration ice cover freeze-up and break-up periods from approximately mid-November to approximately mid-April that may hide a chronic leak from sight. Having a primary leak detection system that can rapidly detect large leaks and a secondary leak detection system that can eventually detect chronic leaks before spring break-up of offshore ice will allow an operator to reduce potential spill volumes to the environment and simplify potential clean-up efforts. Introduction The leak detection system selection philosophy for arctic pipeline projects can be different than for ice free, warmer climate, offshore pipeline projects. For example, a deepwater Gulf of Mexico or offshore West Africa pipeline project team may consider computer-based leak detection systems. These systems collect data from temperature instruments, flow meters, pressure instruments, and in some cases, density instruments. These instruments are located, as a minimum, at the inlet and outlet of a pipeline and at any major branches receiving or sending flow to/from another source or destination. These systems are considered "internal" leak detection systems, because they depend on internal measurements and trends or predictions of the internal measurements to monitor the pipeline for potential leak events.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".