Integrated Assessment of CO2-Enhanced Oil Recovery and Storage Capacity
Bibliographic record
Abstract
Abstract CO2 enhanced oil recovery (EOR) has been used as a commercial process for enhancing oil recovery since the 1970s, whereas limited field applications of CO2 storage were undertaken only recently. In practice, considerable reservoir engineering design effort was made to reduce the total amount of CO2 required to recover each barrel of oil in a CO2 EOR project. For CO2 storage, however, the objective is to increase the amount of CO2 left behind at the end of the injection process; therefore, the approach to the design question changes. Consequently, optimization of CO2 EOR and CO2 storage processes differs significantly from the current CO2 injection practices. In this paper, techniques were developed to systematically assess CO2 EOR and storage capacity in a hydrocarbon reservoir selected for a demonstration project. More specifically, oil recovery was assessed and determined under miscible conditions, while CO2 storage capacity was determined by using an estimation model improved in this study. In addition, economic analysis was conducted, assuming that CO2 was captured from a chemical plant and transported 120 km to the oilfield. It is found that the geological framework is suitable for CO2 storage in the selected reservoir and that, due to a favourable CO2 miscible displacement mechanism, high oil recovery and storage capacity can be achieved, which leads the demonstration project to be economically profitable if prices of crude oil and CO2 remain above certain values.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".