Optimizing the development plan for oil production and CO2 storage in target oil reservoir
Bibliographic record
Abstract
Carbon dioxide enhanced oil recovery (CO 2 -EOR) technology is used for oil production and CO 2 storage in reservoirs. Methods are being constantly developed to optimize oil recovery and CO 2 storage during the CO 2 displacement process, especially for low-permeability reservoirs under varying geological conditions. In this study, long-core experiments and trans-scale numerical simulations are employed to examine the characteristics of oil production and CO 2 storage. Optimal production parameters for the target reservoir are also proposed. The results indicate that maintaining the pressure at 1.04 to 1.10 times the minimum miscible pressure (MMP) and increasing the injection rate can enhance oil production in the early stage of reservoir development. In contrast, reducing the injection rate at the later stages prevents CO 2 channeling, thus improving oil recovery and CO 2 storage efficiency. A solution-doubling factor is introduced to modify the calculation method for CO 2 storage, increasing its accuracy to approximately 90 %. Before CO 2 breakthrough, prioritizing oil production is recommended to maximize the economic benefits of this process. In the middle stage of CO 2 displacement, decreasing the injection rate optimizes the coordination between oil displacement and CO 2 storage. Further, in the late stage, reduced pressure and injection rates are required as the focus shifts to CO 2 storage. • The micro-mechanisms of CO 2 flooding and sequestration under varying miscibility degrees. • A comprehensive factor for evaluating CO 2 displacement and sequestration effectiveness is proposed. • Refinement of mathematical models for CO 2 displacement and sequestration across different production stages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".