Oil production characteristics and CO2 storage mechanisms of CO2 flooding in ultra-low permeability sandstone oil reservoirs
Bibliographic record
Abstract
Using the ultra-low permeability reservoirs in the L block of the Jiangsu oilfield as an example, a series of experiments, including slim tube displacement experiments of CO 2 -oil system, injection capacity experiments, and high-temperature, high-pressure online nuclear magnetic resonance (NMR) displacement experiments, are conducted to reveal the oil/gas mass transfer pattern and oil production mechanisms during CO 2 flooding in ultra-low permeability reservoirs. The impacts of CO 2 storage pore range and miscibility on oil production and CO 2 storage characteristics during CO 2 flooding are clarified. The CO 2 flooding process is divided into three stages: oil displacement stage by CO 2 , CO 2 breakthrough stage, CO 2 extraction stage. Crude oil expansion and viscosity reduction are the main mechanisms for improving recovery in the CO 2 displacement stage. After CO 2 breakthrough, the extraction of light components from the crude oil further enhances oil recovery. During CO 2 flooding, the contribution of crude oil in large pores to the enhanced recovery exceeds 46%, while crude oil in medium pores serves as a reserve for incremental recovery. After CO 2 breakthrough, a small portion of the crude oil is extracted and carried into nano-scale pores by CO 2 , becoming residual oil that is hard to recover. As the miscibility increases, the CO 2 front moves more stably and sweeps a larger area, leading to increased CO 2 storage range and volume. The CO 2 full-storage stage contributes the most to the overall CO 2 storage volume. In the CO 2 escape stage, the storage mechanism involves partial in-situ storage of crude oil within the initial pore range and the CO 2 carrying crude oil into smaller pores to increase the volume of stored CO 2 . In the CO 2 leakage stage, as crude oil is produced, a significant amount of CO 2 leaks out, causing a sharp decline in the storage efficiency.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".