Bibliographic record
Abstract
美国的页岩气开发引发了全世界范围内的页岩气开发热。加拿大和中国也加快了页岩气开发和生产的脚步。页岩气的发展将极大地影响着全球天然气市场。本文在分析了影响页岩气开发资源、技术和经济因素，结合常规油气经济评价方法，建立了基于净现值法的页岩气开发经济评价模型。本文选取宾夕法尼亚州2011年开钻的连续4年生产且年均生产期超过11月的115口页岩气井作为经济评价的数据样本，并根据最终可采储量的预测将样本分成P1-P4四个不同的资源等级，同时预测了未来价格从而得出开发周期内的产出。通过分析宾夕法尼亚州页岩气开发的各项投入和费用来计算投入，并根据投入和产出计算不同资源等级气井项目生命期内各年的净现金流量，得出最终的净现值指标，体现了不同资源等级气井的经济性。并对经营成本和气价进行了敏感性分析，综合分析各种不同取值情况下气井的经济表现。 Shale gas development in United States led to a global shale gas development boom. Canada and China are developing and producing the shale gas. Development of shale gas will influence the global natural gas market. Based on analyzing the resources, technology and economic factors that influence the effect of shale gas development and referencing the economic evaluation method for conventional oil and gas, this paper established shale gas development economic evaluation model based on NPV method. We choose 115 wells with more than 11 months production period per year individually from 2011-2014 in Pennsylvania as evaluation sample, which can be divided into P1-P4 grade according to the EUR characteristic. Using the first four years of real output, price data and forecasts about the future price and output, this paper calculates the output of the development cycle; and through the analysis of the various inputs of shale gas development in Pennsylvania and charges, we can predict the monetary input. Finally, we can utilize the input and output parameters to calculate the net present value (NPV) index of wells in different production grade. And the sensitivity analysis was carried out on the gas price and operation cost to discuss the economy of wells in different scenarios.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".