Economic Analysis of Low Salinity Polymer Flooding Potential in the Niger Delta Oil Fields
Bibliographic record
Abstract
With the current growing demand for oil, oil price and the concerns about future oil supplies increases the pressure in securing oil resources. Enhanced oil recovery processes are applied to recover oil not produced by natural and secondary energy drive of the reservoir. In this study, Simulation has been carried out on a hypothetical model using (ECLIPSE 100) as the simulator. Three cases natural depletion, waterflooding, and injection of low saline polymer were considered.5-spot pattern of four vertical producers wells and one vertical injector well was used as a hypothetical well model. Economics analysis were carried out in this three scenario to determine their net present value, profit per dollar invested, payout and Discounted flow-rate of return. The results shows that low salinity polymer flooding has the highest recovery of 62% and profit with NPV @ 10 ($412.9MM), payout 0.9 years, profit per dollar invested $25.9 and dcf-ror 82%. However, waterflooding gave recovery of 42%. NPV @10 ($ 317.3MM), payout 1.2 years, profit per dollar invested $20.8, dcf-ror 78%. Natural depletion gave recovery of 16.5 %, profit with NPV @10 (230.0MM), payout 1.0 years, profit per dollar invested $9.3, dcf-ror 78%. Decision rule was applied using NPV, DCF-ROR, NCR and payout which states that project with higher NPV, DCF-ROR, NCR and less Payout are more economically viable. The result of the three cases considered shows that low salinity polymer injection is more profitable followed by waterflooding and natural depletion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".