Real-Time Digital Interpretation of Subsea-Blowout-Preventer Tests
Bibliographic record
Abstract
Summary A computer-based method expedites interpretation of pressure data during subsea blowout-preventer (BOP) tests. This can reduce the time and cost of current subsea-BOP testing practices in a safe and objective manner. Currently, individual tests can require more than 1 hour of shut-in time, and a complete series of subsea-BOP tests may comprise at least 12 individual tests. The digital method employs computer software to produce an accurate model of the pressure-decline behavior relatively early in each test. The model can thus predict if future pressures will stabilize at an acceptable level. With regulatory approval and a reliable method to forecast pressure, the duration of subsea-BOP tests can be reduced significantly. Comparison of the digital method to conventional subsea-BOP testing on numerous field trials shows excellent agreement. If implemented, the digital method could save hours of valuable critical-path rig time during every series of subsea-BOP tests. Working in concert with regulatory authorities to gain endorsement of this method is integral to the project. Functionality of the software, example results, and implementation status are reported.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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".