Economics Analysis on the Development of Nigerian Offshore Marginal Fields Using Probabilistic Approach
Bibliographic record
Abstract
Marginal Field Development in the prolific Niger Delta environment is of strategic importance to the Federal Government of Nigeria’s drive towards aggressive Reserve and Production Capacity enhancement. The objective of this study is to provide a perspective on portfolio diversification, investment and resource development on offshore marginal field in Nigeria. The economic analysis was carried out deterministically using economic indices like Net Present Value, Internal Rate of Return, Present Value Rate and others. Probabilistic model was also incorporated to assess the impact of the uncertainties in the input parameters using Monte Carlo simulation 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 $526,749,924.84 at a discount value of 15% and Internal Rate of Return at 60%. Probabilistically, certainty of having a positive net present value (NPV) and good internal rate of return (IRR) values far above the hurdle rate for investment in Nigeria was obtained. The sensitivity analysis outlined oil price and tax rate as key sensitive parameters in maximizing profit. These clearly showed that the development of offshore marginal fields in Niger Delta of Nigeria is economically viable. Key words: Offshore marginal field; 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) | 0.000 | 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 | 0.000 | 0.001 |
| 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".