Enhancing the Effectiveness of Vertical Water Injection Wells With Inflow Control Devices (ICDs): Design, Simulation and Economics
Bibliographic record
Abstract
Water injector completion techniques used traditionally, such as frac packs or openhole standalone screens, were judged to be incapable of meeting all completion objectives and have been reported to loose injectivity over time coupled with the issue of long term injection conformance due to plugging. Another major challenge is to achieve even distribution of the injected water into all zones along the wellbore. Permeability contrasts, formation damage, creation of thief fractures, and changes in wellbore injectivity need to be managed to avoid early breakthrough in adjacent production wells. This study presents the application of inflow control devices (ICDs), fined tuned by reservoir simulations for balancing the water injection profile into various sand formation zones in an open–hole completed injector well in Flo-Z6, a stratified Niger Delta reservoir with communicating layers. The solution targeted at developing a screening tool for deciding candidate layers in Flo-Z6 reservoir and installing special flow control devices, tailor-made for injection wells and with correct nozzle sizes for this particular case. The results from this study show that, the installation of ICDs with different nozzle configuration in the injector wells tailored to equalize the water outflow (for better sweep efficiency), improved the field oil recovery by 11.9% (6.6MMstb). Economic indicators used to validate the profitability of the investment further showed that completing the injectors with different ICD nozzle configuration was more profitable, with an NPV@10% of $192.5million, profit per dollar invested of $6.6, DCF-ROR of 81% and a pay-out period of 1.2 year which is relatively short.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".