Development of Recovery Factor Model For Water Drive and Depletion Drive Reservoirs in The Niger Delta
Bibliographic record
Abstract
Abstract Recovery factor of an oil reservoir is paramount for accurate reserves estimation and field development planning. It is usually estimated using expensive simulation or experimental studies. However, most published models account for only primary recovery. This study is therefore designed to develop a correlation model that can estimate recovery factor under both primary and secondary recovery from oil reservoirs in the Niger Delta having water and depletion drive mechanisms. For this study, the models for recovery factor were established using statistical correlation of data collected from 136 oil reservoirs in the Niger Delta. A sensitivity analysis was performed on different parameters affecting recovery factor of water and depletion drive reservoirs. The results obtained were compared to other published models. The results show that for both water and solution gas drive reservoirs; oil viscosity and residual oil saturation do have a strong correlation with recovery factor, while pressure, API gravity and gas oil ratio do have a strong correlation with recovery factor only in solution gas drive reservoirs. Results also show that no statistical correlation exists between formation volume factor, reservoir thickness, porosity, permeability, initial water saturation, temperature, water viscosity and recovery factor. The novelty of the recovery factor models is its ability to estimate secondary recovery factor for oil reservoirs that have been subjected to water injection. However, the models developed in this study should be valid also to oil reservoirs in other regions having similar geological characteristics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".