Numerical simulation of viscous fingering in CO₂ storage: Addressing the limitations of laboratory-derived relative permeabilities
Bibliographic record
Abstract
• None of the published CO 2 /water Relperms produced pronounced viscous fingering. • The proposed Relperm curves for water and CO 2 successfully generated finger patterns. • The key difference between the proposed and lab-derived relative permeabilities is the total mobility at the shock-front gas saturation. • The experimentally derived Relperm curves in all cases were “over stable”. Gas injection in CCS is an unstable displacement process due to the viscosity contrast between injected gas and in-situ brine, a fact often overlooked in CO₂ storage modelling. Most studies estimating relative permeability (Relperms) for CCS rely on conventional methods. This paper applies a novel approach that explicitly considers viscous instability from injecting low-viscosity gas into more viscous brine. Based on our earlier studies (A. Beteta et al., 2024; Sorbie et al., 2020). viscous fingering is expected under viscous-dominated conditions. While strong capillary forces may suppress fingering at lab scale, the balance of viscous and capillary forces can shift at field scale, leading to pronounced gas fingering. To model CO₂ injection, we used published CO₂/water Relperms from CCS experiments. Two approaches were compared: conventional Relperms estimation and an alternative viscous fingering-based method. Simulations using conventional CO₂/water Relperms showed no fingering patterns, whereas our earlier study indicated that core flood experiments with a water–CO₂ viscosity ratio of ∼55 may generate gas fingers. The second approach, based on maximum mobility, produced gas fingers and matched production and differential pressure equally well. This apparent absence of fingering may suggest limitations in the conventional representation of flow dynamics under such conditions. Possible reasons why experimental relative permeabilities do not capture the expected “fingering behaviour” are discussed in this paper. The main difference between our proposed Relperms and lab derived Relperms is in the total mobility, with our Relperms having a higher total mobility at the shock front gas saturations (S gf ) of CO 2 .
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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 | 0.001 |
| 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) | 0.000 | 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".