Economic Analysis of Liquefied Natural Gas Floating Production Storage and Offloading Plant (LNG FPSO) Using Probabilistic Approach
Bibliographic record
Abstract
The global yearning for clean and safe environment coupled with the need of monetizing stranded gas fields to meet the growing demand of Natural gas in the world today has called for understanding of the range of potential for commercial realization of Liquefied Natural Gas Floating Production Storage and Offloading Plant (LNG FPSO). This places a heavy burden on the economic evaluation process which will give the maximum insight into the basis for a decision to invest or not to invest in the LNG FPSO. An economic analysis of 5.2 million tonnes per annum (MTPA) LNG FPSO plant was undertaken. A Monte Carlo simulation method was adopted in this study through the use of Crystal Ball Software. The key uncertainties were represented and their respective impacts on economic viability defined. The deterministic model results obtained from the studies were very impressive with Net Present Value of $2.3 billion at a discount value of 15% and Internal Rate of Return at 32.68%. Probabilistically, 74.96% certainty of having a positive net present value (NPV) and good IRR values far above the hurdle rate for investment in Nigeria was obtained. These clearly showed that LNG FPSO is profitable. Certainty of payback period of not exceeding 5 years was obtained to be 55.89%. Key words : LNG FPSO; Offshore LNG liquefaction; Probabilistic approach; Sensitivity analysis; Economic yardstick
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".