SEAR JIP: A Success Story of Collaboration and How to Improve Equipment Reliability on Subsea Production Systems
Bibliographic record
Abstract
Abstract The award-winning Subsea Equipment Australia Reliability (SEAR) Joint Industry Project (JIP); is a partnership led by Wood with participation from a group of operators namely Chevron Australia, ConocoPhillips, Inpex, Santos, Shell Australia and Woodside. Now delivering Phase 6, the JIP is focused on collaboration and knowledge sharing in order to improve the competitiveness of Australia's oil & gas industry by addressing critical challenges associated with subsea equipment failing prematurely. This paper will provide an overview of the SEAR JIP and outline lessons learned, and value created. Results from the reliability database will be presented as well as findings from ongoing field trials on the four living laboratories deployed in different geographic locations and water depths in Northern Australia. This paper will also discuss challenges associated with subsea controls umbilicals that are prone to emit hydrogen gas and fluids at the surface facility through the electrical junction boxes. The end goal of SEAR JIP is to develop an industry wide recommended practice, with regional guidance notes for equipment and field design. The recommended practice is intended to reduce operating cost for existing and future projects, while identifying technologies that are specific to Australian waters.
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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 | 0.029 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".