Assessment of Economic Viability of Various Field Development Strategies
Bibliographic record
Abstract
Abstract This paper presents a comprehensive perspective for comparing field development strategies (i.e. setting up oil field for production) by outlining a simple approach to help the integrated asset team in making decision on which method would be most appropriate. The field development options considered were natural depletion, water injection, gas injection, and water alternate gas (WAG). UBED field was discovered in 1974, water depth is 130 m with initial reservoir pressure of 446 bars and 35,681,991 m3 estimated oil initially in-place (OIIP). Preliminary assessment using the material balance indicated about 25% ultimate recovery. Each scenario was optimized for maximum hydrocarbon recovery at the lowest cost per barrel by optimizing of wells, and critical gas saturation (i.e. 0% and 10% respectively). Economic analysis was performed using NPV, profitability index, payback period, and IRR on the cases. For each scenario, a plateau rate of 15% is maintained per year at a production rate of 3500 m3/d as benchmark. The various economic impacts on the project were determined and the results are presented. Results shows that estimated ultimate recoveries (EUR) were 30.9%, 53%, 37.2% and 53.5% for natural depletion, water injection, gas injection, and water alternate gas injection schemes respectively. The best production option for UBED reservoir lies between water injection and WAG injection. The comparison between the two scenarios shows that WAG is less costly and to develop the reserves while recovery, and profits are very high. Production plateau can also be sustained economically for longer period with shorter pay back on investment. In addition, WAG shows better EUR, a shorter production plateau of about 4% increases in over the water injection. This increase was not offset by profit indices, and payback. In this scenario, a gas compressor was installed which resulted in an increased capital expenditure (CAPEX). The revenues, expenses, and profits from each scenario are compared for the various development options at the end of the section and a proposed plan is selected based on economic parameters. The comparative economic assessment of the UBED field case in this study adequately addresses critical cost parameters relevant to field development studies, and integrated comparison of profitability indicators from which reliable economic decision can be reached.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".