The Analysis of Compositional Effects on Global Flow Regimes in CO2 Near-Miscible Displacements in Heterogeneous Systems
Bibliographic record
Abstract
Abstract This study investigates how compositional effects interact with the flow behavior during near miscible (and immiscible) CO2-oil displacements in heterogeneous systems. A series of numerical simulations modeling 1D slim-tube and 2D areal systems were performed using a fully compositional simulator. With negligible numerical dispersion, the fine-scale (Δx=0.005m) slim-tube simulations were performed to provide the "truth case" in terms of the compositional effects and oil/component recovery. A number of grid resolutions were tested to examine cell-size effects on the simulation accuracy. It was found that coarse cell size not only leads to spreading of the displacing front, but also lowers the displacement efficiency by reducing the component stripping effects, as noted by Orr (2007). The corresponding 2D cases are based on a small heterogeneous sector model of dimensions 50m × 10m, in order that the finest scale displacement physics can be modelled accurately. We investigated various flow regimes ranging from viscous fingering to channeling displacements within heterogeneous random correlated fields. CO2 dissolves in oil at near-miscible conditions and improves the mobility of the oil, but leads to earlier breakthrough of CO2 in both fingering and channeling flow. It was also found that the instability of fingering flow could introduce considerable variation in the composition paths during oil displacement by CO2, compared with the slim-tube simulations, particularly in the later stages of the flood. For this reason, heavier component recovery is more likely to be affected and reduced by viscous instability. In the case of channeling flow, compositional effects were less important since the permeability channel dominated the displacement. Both the ultimate oil recovery and component recovery are significantly and about equally reduced, when the underlying heterogeneity is of dominant influence. To summarize, compositional effects can have a very significant impact on the prediction of near-miscible CO2 EOR projects. Issues such as front stability, local displacement efficiency and formation of fingering/channeling during CO2 near-miscible displacement can lead to behavior that is significantly different from immiscible flooding in these systems. The process of mass transfer between CO2 and oil can be hampered to a certain degree by unstable flow depending on the level of heterogeneity. This leads to a further reduction in component recovery, particularly of the heavier components. Lastly, the appropriate upscaling methods considering mass transfer still require further investigation for CO2 near-miscible displacement in field-scale applications. The complete dataset and results of this study are available online as a model case example for testing out potential upscaling techniques for compositional flows in heterogeneous systems (Wang et al. 2019).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".