Advanced CO2 Sequestration Analysis in Geological Reservoirs
Bibliographic record
Abstract
Mitigating rising atmospheric CO₂ levels is critical to addressing climate change, and geological carbon storage in saline aquifers is a promising solution. This study develops and evaluates three deep learning modelsdeep neural network (DNN), gated recurrent unit (GRU), and recurrent neural network (RNN)to predict the residual trapping index (RTI) and solubility trapping index (STI) via diverse reservoir datasets. The GRU model outperformed both DNN and RNN, particularly in handling long-term dependencies and minimizing error, while DNN showed robust accuracy through effective modeling of complex nonlinear relationships. In contrast, RNN exhibited challenges with gradient instability, affecting its prediction performance. Sensitivity analysis highlighted the significance of input variables like post injection and injection rate, with DNN showing greater dependence on these features. Excluding these variables reduced predictive accuracy, especially for RTI. These findings suggest that GRU is the most effective for predicting CO₂ trapping, offering a valuable tool for optimizing carbon storage strategies and supporting global carbon reduction efforts.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".