Facility Operations Readiness and Project Start-Up Support and Integration
Notice bibliographique
Résumé
Abstract The Australia Pacific LNG (APLNG) Downstream Project comprised construction of a two-train LNG plant, two 160,000 m3 LNG storage tanks, a LNG loading jetty, and associated infrastructure on Curtis Island, near Gladstone, Queensland. The facility utilises ConocoPhillips’ proprietary Optimized Cascade ® process to liquefy natural gas. Project planning began in 2008 and construction commenced in 2011. LNG production from Train 1 began in December 2015 and Train 2 came on line in October 2016. In August 2017, the final 90-day two-train lenders test was successfully concluded and included an operational component in which the LNG Facility operated at ten per cent above nameplate capacity for the 90-day period. This successful outcome was achieved through collaboration, integration and robust execution plans by the APLNG Operations, APLNG Project, ConocoPhillips LNG Technology and Licensing and Commissioning and Start-up work teams. In an effective strategy to support the drive to APLNG first cargo, the combined teams formed a fully Integrated Completions Team in 2015. This team worked together enabling rapid development of the operations workforce capability and resulted in both trains achieving performance tests at first attempt. The APLNG Project operability assurance and operations readiness program commenced with a collaborative workshop, attended by Operations, Projects and Commissioning and Start-up representatives. A focus on high collaboration and integration between the teams backed by ConocoPhillips’ Operability Assurance (OA) and Operations Readiness (OR) principles identified 370 critical business deliverables. Learnings were leveraged from across ConocoPhillips assets (including Darwin LNG) as they pertained to readiness best practice. APLNG Readiness Assurance guidance was established to create specific functional readiness trackers to measure every action supporting the 370 deliverables. A simple and effective tool was developed and supported by robust monthly review forums where each action was tracked and rolled up to S-Curves. These deliverables translated to over 7,000 unique line items tracked over the entirety of the readiness implementation. This work was leveraged by the Integrated Completions Team which implemented robust lessons learned from Train 1 to realize immediate results, delivering a 33% reduction in the Train 2 start-up schedule. The combined efforts of the teams delivered two highly operational trains within the 2016 calendar year resulting in the production of additional cargoes. The successful execution and implementation of OA/OR principles combined with strong integration of APLNG Operations into the APLNG Project phase laid the foundation for exceptional first year operational and production performance. This meant the APLNG Operations Team moved from a newly formed team to a high functioning and critically evolving team in support of the APLNG Downstream LNG Facility.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».