Comparison of various reactive transport simulators for geological carbon sequestration
Bibliographic record
Abstract
The capabilities of reactive transport modeling codes for geological carbon sequestration have improved significantly in the past decade. Comparing different geochemical modeling codes is crucial to identify modeling discrepancies, especially when experimental validation is not feasible. However, it is challenging to consistently get comparable results, as shown in previous studies where batch reaction of CO 2 storage using different simulators sometimes resulted in significant discrepancies in their outputs. In this study, we introduce and demonstrate an approach to consistently produce comparable batch-reaction modeling of kinetically controlled CO 2 -water-rock interactions in PHREEQC, TOUGHREACT, and GEM, which are amongst the most widely used simulators for CO 2 sequestration studies. The primary step is to assemble a thermodynamic database in PHREEQC format, with representative fluid properties for CO 2 -water interaction, and carefully convert it to the format of the other simulators. We use two case studies from the literature to demonstrate our method where good matches between the outputs of all three simulators were achieved, which was not previously attained. Furthermore, limiting the discrepancies in batch-reaction models provides a consistent baseline to study the coupled mechanisms of transport and chemical reaction, which was also successfully demonstrated with a one-dimensional reactive transport model in PHREEQC, GEM and TOUGHREACT.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".