Thermally Compensated Leak Detection Results in Significant Blow Out Preventer (BOP) Testing Efficiencies
Bibliographic record
Abstract
Abstract Operators, service provides, and drilling contractors established an industry consortium to advance the art of leak detection. The initial development focused on software-based testing of BOPs in deepwater. Upon completion of the pilot program on five deepwater rigs, the results exceeded original expectations. The test results proved the software was effective, efficient, and rig friendly. Once the template was filled out, with the click of the "Start" button, the software was completely automated, up to the time of printing the report. Through an iterative and collaborative process a unique solution for the Low Pressure (LP) and High Pressure (HP) tests has been developed. Due to the subjective nature of the Circular Chart Recorder (CCR) methodology for validating a test, a small leak may not be identified for up to 30 minutes into the HP test. With this new methodology, objective identification of a slow leak normally occurs during the LP test in less than three minutes; and a good test (no leak) typically validates in the regulatory agency's minimum holding time requirements. The software provides greater assurances, transparency, and reliability as compared to the CCR. The antiquated CCR is easily manipulated in multiple ways, which is now eliminated. This software provides the industry with a tool for objective, efficient test validation. The software generates simple, clear, concise reports and contains more information as compared to what is currently available. It archives tests in a secure format, and the software allows the users to retrieve and review the tests for any required scrutiny. It also prints the reports to a secured PDF format and archives them. This paper discusses the current state of the development along with the associated benefits. A vision of the application development pipeline for further pressure analysis opportunities is also introduced.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".