Multiscale projection-based embedded discrete fracture modeling approach for CO2 storage in deep saline aquifers
Bibliographic record
Abstract
This study introduces a multiscale simulation framework, termed Projection-based Embedded Discrete Fracture Modeling with Algebraic Dynamic Multilevel method (pEDFM-ADM), which integrates an embedded discrete fracture network representation with a fully algebraic, front-tracking-based mesh adaptation strategy. Incorporating a fully implicit scheme, compositional thermodynamics, and algebraic multilevel operators, the framework captures essential subsurface processes such as buoyancy-driven migration, convective dissolution, phase partitioning, and fracture-matrix interactions under geologically realistic conditions. The method constructs a hierarchy of multilevel grids and localized multiscale basis functions that introduce fine-scale heterogeneities at each coarse level. Adaptive mesh refinement and coarsening are driven by local variations in CO 2 mass fraction and executed through algebraic prolongation and restriction operators, enabling efficient projection between grid levels. The framework is systematically evaluated across a sequence of test cases with increasing complexity, including systems with low-permeability flow barriers, highly conductive fractures, striking a trade-off between computational resource and detailed simulation accuracy. Overall, the pEDFM-ADM framework provides a scalable, fully algebraic, and physically adaptive modeling tool for large-scale CO 2 storage simulations in fractured porous media, supporting predictive simulation and risk assessment for long-term carbon sequestration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".