Optimizing Water Injection Operational Parameters for Improved Oil Recovery
Bibliographic record
Abstract
Abstract The demand for energy has been on a steady rise and oil production from world reserves remains the major source of energy generation; therefore, primary recovery methods alone are insufficient to sustain economic oil production. By supplementing the natural energy of the reservoir through secondary recovery techniques, an incremental recovery factor ranging from 15% to 25% usually can be achieved. This makes water injection feasible and economically attractive as it would allow for increased production rates. This paper is focused on the incremental recovery that can possibly be achieved by optimizing the operational parameters of water injection. A reservoir in the Niger Delta region of Nigeria, Reservoir OD-50 was used to illustrate this. Reservoir OD-50 has an estimated STOIIP of 267MMbbls and a recovery factor of 32%, and is planned to be developed alongside water injection for pressure maintenance and improved recovery. This work was done to determine the additional oil can be produced by optimizing the operational parameters affecting the efficiency of water injection. Parameters such as injection rate, time of commencement of injection, contributing drain lengths, well types, bottom hole pressures and tubing head pressures were studied as sensitivities in this work and an optimized case with the most influential parameters on the recovery was obtained. The optimized case resulted in an additional oil recovery factor of 1% and from the economic analyses, the NPV was increased from $293.06M to $305.25M, the IRR from 27.1% to 27.4%, and the PI from 1.44 to 1.46.
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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 | 0.001 |
| 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".