Investment Strategy of CO2-EOR in China: Analysis Based on Real Option Approach
Bibliographic record
Abstract
Abstract As the most potential Carbon Capture, Utilization, and Storage (CCUS) technology, CO2-enhanced oil recovery (CO2-EOR) can both improve oil recovery and relieve the pressure of reducing CO2 emission. However, CO2-EOR projects have not been substantially deployed in China due to the significant investment and high uncertainties of technology, market, and policy. Therefore, identifying potential bottlenecks, and developing effective investment strategies are of great necessity at present. In this work, a real option approach combined with reservoir simulation technologies is proposed, which can investigate the optimal deployment timing and the investment value of the CO2-EOR projects. Meanwhile, a sensitivity analysis is conducted to examine the effects of different uncertainties. The results show that real option approach is suitable for the evaluation of CO2-EOR projects because it can fully take the flexibility of investment time into account. And it is found that under the current investment environment, it is difficult for China to deploy CO2-EOR projects on a large scale before 2030. High oil prices, low CO2 purchase prices, and transportation of CO2 by pipeline can bring forward the investment time and increase the investment value. Besides, government subsidies and technological progress are also favorable for the deployment of the project. Compared with technological progress, the effect of subsidies is more obvious, while it should be noted that huge subsidies will bring a financial burden to the government. In a word to launch CO2-EOR projects earlier and make it play a more important role in China's carbon emission reduction, a compound strategy should be made based on consideration of all these influencing factors.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".