Project integrated LNG offloading availability assessment for FLNG
Bibliographic record
Abstract
This extended abstract explains a combined heuristic, analytical and probabilistic process to evaluate LNG offshore offloading availability in combination with facility uptime and commercial drivers such as LNG sales/supply contracts. The heuristic assessment is informed by facility operators’, LNGC masters’ and tug operators’ experiences in offshore offloading and berthing operations. The analytical process includes assessment of met-ocean, mooring, manoeuvrability simulation, model testing and event forecasting methods. Gaps about uncertainties for future predictions are filled by probabilistic Monte-Carlo simulations. The heuristic, analytical and probabilistic approach, combined with commercial drivers, is put together into uptime assessment to forecast the techno-commercial performance of the facility. The uptime assessment enables:confidence on achievable LNG throughput, the best for facility configuration and size, the best for facility location and facility’s operational expenditures;contractual viability—for LNG supplier and gas off-taker; and,key to terminal performance guarantee to gas off-takers. This process has been developed within INTECSEA during the past six years and has been applied to more than 15 LNG offshore offloading facilities at varying geographical locations. This extended abstract explores the key drivers and describes the effect on those key drivers due to varying location, varying technology or LNG sales/supply contracting strategy. The key drivers include: achievable LNG throughput, uptime, downtime, demurrage, cargo cancellation, facility downturn and partial LNG offloading. The process described is specific to side-by-side offloading operations; however, it can also be adapted to standard jetty offloading operations and tandem offloading operations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".