Bibliographic record
Abstract
Pore-throat networks of porous rock samples are constructed from analyses of 3D X-ray computed micro-tomographic (CMT) images of three rock core samples taken from the Viking field in the Alberta basin. The networks are extended to network flow models in order to characterize the properties of reactive flows through porous media. New to both the CMT and network flow work is the extraction of four material phases: the void phase; kaolinite; quartz; and “minerals of interest”. Thus, the segmented images contain information on mineral abundances and accessibilities of the four phases: cluster sizes; accessible surface areas; size and area distributions. The standard network flow model is extended to include the mineral distribution network for computation of reactive flow. Reactions are chosen to simulate precipitation and dissolution reactions that may accompany CO2 sequestration. The minerals of interest are assumed to be anorthite. The reactive model includes both kinetic and instantaneous reaction components. The reaction rates for kinetic components are integrated over each time step, and the equilibrium condition for the instantaneous components is satisfied at every time step. The reactive flow model is applied to the Viking samples. The simulation results show that there are differences from previously reported results in the literature. Small reactive surface areas of anorthite result in a slow change in the kaolinite reaction rate; the time to reach a steady state of the kaolinite reaction is on the order of 103 seconds. The anorthite reaction rate depends only on pH because of the small values of saturation state. Hence, the pore scale variation of anorthite reaction rate at steady state is small. The simulation results indicate there are heterogeneities in the kaolinite reaction rate, which depends on the saturation state. By inspecting the saturation state, the heterogeneities in the kaolinite reaction rate can be predicted.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".