A Scalable Parallel Compositional CO2 Geological Storage Simulator
Bibliographic record
Abstract
Abstract Storing CO2 in deep saline aquifers is one of the most promising methods for achieving carbon neutrality. Numerical reservoir simulation can assist researchers and engineers in comprehensively understanding and effectively managing the CO2 sequestration process, thereby ensuring its safety and effectiveness. Most of the current CO2 storage simulators use a CO2-brine fluid model which only has water and CO2 components. When impure CO2 is injected, the subsurface fluids form a multi-component gas-brine system, whose phase behavior cannot be accurately described by a CO2-brine fluid model. The current gas-water compositional models include a very limited number of gas components. In this study, we developed a fully implicit parallel CO2 storage simulator for distributed memory computers based on our in-house parallel platform. This simulator uses a fully compositional fluid model which treats water as a component and incorporates it into phase equilibrium calculations. The gas components include not only CO2 but also other common components in captured impure CO2, such as CH4, N2 and H2S. The cubic Peng-Robinson equation of state was used to predict the fugacity and PVT properties of CO2-rich phase. The fugacity in aqueous phase and brine properties are calculated by Henry's law and empirical correlations respectively. Meanwhile, the effect of dissolved gas influence on aqueous viscosity is considered which is ignored by current commercial simulator. The finite difference (volume) method is applied to discretize the compositional fluid model. Numerical experiments show that our simulator is scalable, stable and validated to simulate large-scale CO2 storage problems with hundreds of millions of grid blocks on a parallel supercomputer cluster.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".