Integrated Global Optimization of Displacement Efficiency in Hydrocarbon Reservoirs
Bibliographic record
Abstract
Abstract In this paper, an integrated numerical technique is presented to implement global optimization of displacement efficiency in hydrocarbon reservoirs. This technique chooses the net present value (NPV) as an objective function, which accounts for production- and injection- performance as well as reservoir performance. The flowing and various artificial lifting methods are incorporated into the production performance models, which have been successfully applied in more than forty oil fields. Meanwhile, the reservoir geological model is improved by continuous monitoring and surveillance. Then the objective function is maximized to generate the optimum field production-injection strategies at different development stages using a hybrid genetic algorithm (GA). Such an integrated technique can maximize the displacement efficiency in a fixed well pattern and/or an oil field under different practical constraints. This technique is applied in a water-alternating-gas (WAG) miscible flooding reservoir, and the field performance has shown that the displacement efficiency is significantly improved and the production-injection rates are well controlled. The field water-cut remains low and stable, though the gas-oil ratio is slightly higher than the original ratio. This method can be applied to develop the optimum production- and injection- strategies in a hydrocarbon reservoir so that the displacement efficiency is maximized and the reservoir life is extended.
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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 | 0.001 |
| 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".