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Record W2023542860 · doi:10.2118/81998-ms

Economics of Nigerian Marginal Oil Fields

2003· article· en· W2023542860 on OpenAlexaff
Oluropo Rufus Ayodele, Samuel Frimpong

Bibliographic record

VenueSPE Hydrocarbon Economics and Evaluation Symposium · 2003
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCapital Investment and Risk Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNet present valueDiscounted cash flowProfitability indexCash flowPresent valueInvestment (military)Marginal costEconomicsMarginal valueEconomic riskEconomic analysisOil fieldValue (mathematics)Internal rate of returnFinanceActuarial sciencePetroleum engineeringEngineeringComputer scienceMicroeconomicsAgricultural economicsProduction (economics)

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.025
GPT teacher head0.223
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2003
Admission routes1
Has abstractyes

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