Bibliographic record
Abstract
Weyburn油田位于加拿大Saskatchewan东南,从2000年9月以来一直是世界上最大的CO2驱项目之一。本文用Weyburn油田过去的水驱动态数据建立经验公式,用于预测CO2驱动态。基于Wey-burn油田的注CO2方案,给出了两种不同的关系式。第一种关系式基于垂直井的WAG(水气交替注入)方法,第二种关系式基于水平井注CO2而垂直井注水的情况。第一步,收集和分析1958—2004年的生产数据。用水驱和CO2驱期间的产油量、注水量以及CO2注入量来推导公式。用KinderMorganCO2Scoping模型和油田实际生产数据检验垂直井注水和CO2的经验模型。对比分析表明此简易公式和KinderMorgan模型之间有12%的误差。对于水平井注CO2,不能用KinderMorgan模型对此公式进行检验,但此公式与油田实际生产数据非常接近。根据先前水驱动态数据和CO2注入量,新模型可作为有效筛选工具来预测Weyburn油藏任意地区的CO2驱动态。因此,它对石油公司来说,既省时又经济。同时,与Weyburn油田历史和性质相似的有潜力进行CO2驱的油藏也可使用该公式。
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".