Numerical approach on production optimization of high water-cut well via advanced completion management using flow control valves
Bibliographic record
Abstract
Abstract With the development of smart downhole control devices, such as the electric flow control valve (FCV), research on completion optimization using FCV control is gaining traction for successful field production management. Applying and verifying its applicability to actual assets with uncertain production issues occur are important. This study focuses on managing downhole devices to optimize fluid production in an actual onshore oil field in Alberta, Canada. The target field has been in production operation for over 20 years, and water flooding was used in the early stages of production to maintain reservoir pressure. However, according to the flow characteristics of the field, water injection caused a high water-cut issue due to water channeling. To mitigate the problem, proactive and reactive strategies were investigated to optimize FCV control. Additionally, the effect of completion optimization was estimated considering both the field-level economic value and the fluid production behavior at the device level. In most optimization cases, the cumulative water production could be reduced compared with the base case without valve control. Notably, the flow-balancing strategy increased the revenue of the target field by approximately 23 MM$ by maximizing oil production and suppressing water production. However, reactive and streamline-balancing strategies, which directly control and delay water production, undermined the economic value due to the decrease in oil production. The findings imply that FCV control strategy of suppressing only water production for the field with high water-cut could not be the optimal solution considering the reduction in oil production and the field’s revenue. The results of this study could be used as a reference to optimize downhole devices when applying water flooding in fields where high water-cut is expected.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".