Bibliographic record
Abstract
Abstract In this paper, a new contractual agreement is proposed for the development of marginal oil fields in the Nigerian prolific hydrocarbon sedimentary basins. The proposed agreement is a modification of the existing agreements taking into consideration the special nature of marginal oil fields. Detailed economic analyses were carried out to assess the feasibility of the agreement. The economic analyses involved cash flow modeling, project profitability analysis, project sensitivity analysis and risk modeling using available and generally accepted economic, financial and technical data about the Nigeria operating environment. The final results from this study show that investing in the development of Nigerian marginal oil fields is a worthwhile option. The results show that the proposed agreement leads to favorable return on investment for all the parties involved. Project sensitivity analysis shows that if the combined cost of seismic survey and signature bonus is increased by more than 10%, the project becomes uneconomic. Also, if the price of oil falls below US$18.07, the project would have to be re-evaluated because the discounted pay back period (DPBP) would exceed the expected project life. Furthermore, risk analysis shows that as the NPV (net present value) increases, the risk level associated with such NPV also increases. Options available for financing marginal oil fields development are also presented.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".