Performance Evaluation of CO2 Huff-n-puff Processes in Tight Oil Formations
Bibliographic record
Abstract
Abstract Techniques have been developed to experimentally and numerically evaluate performance of CO2 huff-n-puff processes for unlocking resources from tight oil formations. Experimentally, core samples collected from a tight formation with a permeability range of 0.27-0.83 mD are used to conduct a series of coreflooding experiments. The performance of four recovery schemes, i.e., waterflooding, immiscible CO2 huff-n-puff, near-miscible CO2 huff-n-puff, and miscible CO2 huff-n-puff processes, is evaluated with the tight core samples. The waterflooding process leads to a higher oil recovery factor in comparison with the immiscible CO2 huff-n-puff process, while both the near-miscible and miscible CO2 huff-n-puff processes result in higher recovery efficiency compared to that of waterflooding. Theoretically, numerical simulation is performed to match the experimental measurements obtained in the different recovery schemes. There exists a generally good agreement between the experimental measurements and simulated results. The tuned numerical model is then employed to optimize the injection pressure and soaking time during CO2 huff-n-puff processes. It is found that the optimum injection pressure of the CO2 huff-n-puff process can be set around the minimum miscibility pressure (MMP) between crude oil and CO2, while the soaking time can be optimized for maximizing oil recovery.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".