ANALYSIS OF BLOWOUT PREVENTER MAINTENANCE PERFORMANCE ON RIG #55 AND RIG #99 BASED ON DEGRADATION TEST DATA IN "DERE" FIELD
Bibliographic record
Abstract
Blowout Preventer (BOP) digunakan untuk mengatasi risiko semburan liar dengan menutup sumur sebelum terjadinya semburan. Penelitian ini akan menganalisis masalah yang terjadi pada BOP di Rig #55 dan Rig #99 di Lapangan “DERE” menggunakan metode performance maintenance. Data yang digunakan untuk menganalisis kinerja BOP di kedua Rig tersebut adalah data degradation test. Metode performance maintenance akan menghitung MTBF (Mean Time Between Failures), MTTR (Mean Time To Repair), serta availability. Selain itu, akan dilihat penyebab penurunan kinerja dan memberikan rekomendasi optimalisasi perawatan BOP dengan menggunakan diagram fishbone. Hasil analisis menunjukkan bahwa selama beroperasi pada periode tahun 2023 Rig #55, nilai MTBF adalah 11520 menit, MTTR adalah 2160 menit, availability 89%. Rig #99, nilai MTBF adalah 18.720 menit, MTTR adalah 1440 menit dan availability 92%. Sedangkan Rig #99 di tahun 2021 nilai MTBF adalah 40320 menit, MTTR adalah 1440 menit, availability 96%. Faktor kemunduran yang terjadi pada Rig #55 dan Rig #99 dipengaruhi oleh packing element, sehingga perlu dilakukan pengecekan dan penggantian sesuai dengan jam operasionalnya. Aspek lingkungan tempat kerja, kurangnya kebersihan area BOP, usia part yang sudah usia, prosedur pengantian part belum berjalan efektif dan faktor manusia kurang konsentrasi dan kurangnya kepedulian terhadap SOP menyebabkan penurunan kualitas pada BOP.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".