Optimum Design and Control of the Production-Injection Operation Systems in Petroleum Reservoirs
Bibliographic record
Abstract
Abstract This paper presents a systems engineering approach to implementing optimum design and control of the production-injection operation systems. At first, a production performance model including flowing and different artificial lifting methods is modified from a well basis to an oil field basis. Secondly, the modified model, the injection models and surface pipeline network are integrated with a reservoir model in which the reservoir geological model is updated by continuous monitoring and surveillance. Finally, either the oil rate or the net present value (NPV) can be chosen as an objective function and optimized by a non-numerical algorithm so that the global optimum parameters for both producers and injectors are obtained. This systematic approach has been successfully applied in more than forty reservoirs. These field applications show that this technique can determine not only the optimum production operation methods for a newly discovered reservoir but also the optimum production- and injection- strategies for an existing reservoir. The success rate is over 85% for determing proper production operation methods among individual wells in a new reservoir. For a reservoir currently under production, the reservoir pressure can be kept in an appropriate range with slight increase in the water-cut and the gas-oil ratio. Thus such an integrated technique can be applied to increase the oil recovery and to extend the reservoir life.
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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 | 0.001 | 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".