Bibliographic record
Abstract
In 2023, PureHM conducted an emergency inline acoustic leak detection and threat monitoring survey on approximately 9 km (6 mi) of 200-mm (8-in.) carbon steel pipeline that transports crude oil. The operator observed a deviation on their continuous pipeline monitoring (CPM) system, conducted a stand up, and subsequently suspected a leak along this segment. Conventional leak detection methods were unable to pinpoint the location of the suspected leak, and subsequently, the line was purged. Following the purge, PureHM mobilized for an emergency acoustic leak detection and threat monitoring survey. As the leak detection tool required a liquid medium to detect leaks, PureHM collaborated with the operator to prepare a water slug that would function as a carrier for the tool while also providing the liquid medium. The water slug was contained on both ends by cleaning pigs, with the leak detection tool positioned approximately in the middle. The slug and tools were inserted into the pipeline through a standard launcher, traversed the line using nitrogen to push the tool, and were extracted from the pipeline through a standard pig trap. The survey identified three (3) leaks within a 30-m (100-ft) section of the pipeline. The identification of the three (3) leaks allowed the operator to swiftly enact risk mitigation and repair protocols. This paper will detail the process of the inline leak detection survey, the operational challenges and solutions encountered, and implications for future use of leak detection tools in lines without a liquid medium.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".