Facility Operations Readiness and Project Start-Up Support and Integration
Bibliographic record
Abstract
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 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".