Economic appraisal of shale gas resources, an example from the Horn River shale gas play, Canada
Bibliographic record
Abstract
异乎寻常的页岩气体资源的开发包含伴随大商业生产无常的集中的大写的投资。经济估价，召集多学科的工程数据和信息并且为各种各样的开发情形提供多半经济的结果，形成企业决策的核心。这份报纸使用打折的现金流动(DCF ) 在角河盆评估页岩气体开发的经济结果的模型，东北不列颠哥伦比亚，加拿大。通过数字例子，这研究证明单个平均衰落的使用弯因为整个页岩气体戏是从一个随机的钻过程的结果的等价物。企业决定能对越过戏区域的页岩气体生产率的激烈的变化基于用一条单个衰落曲线的一个 DCF 模型脆弱。一个随机的钻模型在很好估计的最终的恢复(EUR ) 拿那些激烈的变化，衰落在经济估价评价进报道，提供为企业决定有用的更多的信息。假定 $4/MCF 并且用 10% 贴现率的天然气水源价格，从这研究的结果建议那随机的钻策略(例如，不考虑 EUR 很好的) ，能导致在场的一张否定的网价值(NPV ) ；而把优先级给钻的历史上更早与更大的 EUR 开发那些井的一个钻的序列能与各种各样的回报导致积极 NPV，预定并且回来(IRR ) 的内部率。在钻假设的随机下面，无损失的价格是有超过支出时间的 10years 的 $4.2/MCF。相反，如果钻的顺序与井 EUR 严格地成正比，结果是有在更高的 IRR 伴随的假定水源价格下面的无损失的价格的更好经济的结果。
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".