A coupled discrete-continuum approach to simulating CO2 migration and dissolution in porous media 
Bibliographic record
Abstract
Carbon capture and storage (CCS) has emerged as a principal emissions reduction technology for the energy transition. Its effectiveness hinges largely on the security of the storage reservoir, which may be susceptible to leakage through permeable pathways such as abandoned wells and faults. Storage failure presents risks of environmental impacts, increases in atmospheric carbon emissions, reduces both the value of carbon credits and public confidence in CCS as a viable technology for the energy transition. Given the importance of storage security, our understanding of CO2 leakage and its fate and transport in overburden must be improved to help in the prediction, detection and assessment of leaks. Shallow groundwater monitoring for dissolved gases can be complicated by multicomponent mass transfer dynamics in the subsurface. As CO2 migrates through the subsurface, much of the mass will partition from the gaseous to the aqueous phase, and conversely background dissolved gases present in groundwater such as N2 and O2 may partition to the gaseous phase, impacting both the evolution of dissolved gas concentrations and the persistence of free-phase gas in the subsurface. This process may also impact the performance of noble gas tracers in groundwater monitoring techniques. There is therefore a need for numerical models capable of accurately predicting the fate and transport of CO2 in the subsurface. However, traditional multiphase flow models struggle to describe the buoyant unstable gas flow regime expected at leak sites, dominated by gravity and capillary forces.Unstable gas flow is characterized by discontinuous gas clusters and sharp variations in gas saturations in space, in contrast with the smooth variation in gas saturation predicted by continuum multiphase flow models. Discrete approaches such as macroscopic invasion percolation (MIP) are better equipped to model unstable gas flow, however they are limited by assumptions of instantaneous gas movement. ET-MIP (Electro-thermal MIP) is a general purpose model which couples continuum-based electrical, thermal, groundwater and chemical species modules with a discrete MIP gas flow module. This coupled approach allows for accurate simulation of slow gas displacement characteristic of shallow subsurface gas releases while simultaneously predicting the dissolution of CO2. ET-MIP has been validated against bench scale experiments and shown to accurately predict gas generation, multiphase transport and capillary trapping – all mechanisms which govern the fate of CO2 in the subsurface. This talk will present comparison of ET-MIP with a bench-scale CO2 injection and dissolution experiment and a sensitivity study showing the effects of key model parameters on CO2 migration. Findings highlight the benefits of using a discrete-continuum coupled approach for simulating CO2 migration in porous media, and the importance of considering background dissolved gases in the subsurface.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".