A compositional formulation for multiphase multicomponent reactive transport modelling of highly coupled systems
Bibliographic record
Abstract
In certain reactive transport applications, strong coupling between geochemical reactions and hydrodynamics exists. Dissolution and precipitation of minerals, such as the conversion between gypsum and anhydrite [1] or the precipitation of nesquehonite during CO2 sequestration [2], as well as gas bubble formation [3] are geochemical processes which modify the multiphase flow dynamics, with direct feedback on reactive transport processes. In addition, heat generation induced by sulphide mineral oxidation can lead to significant increases in temperature [4], impacting flow, transport and geochemical reactions. In these instances, commonly used reactive transport modelling approaches, which rely on decoupling flow and reactive transport processes, have limitations. For density dependent or two-phase flow problems in the presence of a gas phase, the coupling between flow and reactive transport can be accounted for through a Picard iterative approach [3,5,6]. However, this approach is computationally expensive, involving the solution of nonlinear problems multiple times during each timestep, and convergence properties are often poor. More recently, a weak explicit coupling approach was developed to capture the impact of chemistry on flow by integrating water as a component and perform a volume balance calculation [7]. In the current work, a compositional approach is implemented into MIN3P-THCm, in which the flow variables (pressure, density) are expressed based on mass variables. Hence, this global implicit approach does not require solving the flow problem, but instead integrates groundwater flow processes directly into the reactive transport equations. We show that this approach yields very similar results to the commonly used approaches for single and two-phase flow. Finally, we show that, in highly coupled systems, not considering these coupled effects may lead to significant errors in simulating system evolution, highlighting the benefits of the newly developed approach. [1] Jowett, Cathles & Davis (1993). AAPG Bulletin, 77(3), 402-413. [2] Harrison, Dipple, Power & Mayer (2015). Geochimica et cosmochimica Acta, 148, 477-495. [3] Amos and Mayer (2006). Journal of contaminant hydrology, 87(1-2), 123-154. [4] Lefebvre, Hockley, Smolensky & Gélinas (2001). Journal of contaminant hydrology, 52(1-4), 137-164. [5] Henderson, Mayer, Parker, & Al (2009). Journal of contaminant hydrology, 106(3-4), 195-211. [6] Sin, Lagneau and Corvisier (2017). Advances in Water Resources, 100, 62-77. [7] Seigneur, Lagneau, Corvisier & Dauzères (2018). Advances in Water Resources 122, 355-366.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".