Development of prediction models for storage efficiency factor to estimate volumetric CO <sub>2</sub> storage capacity in saline aquifer
Bibliographic record
Abstract
An accurate estimation of the CO2 storage capacity in saline aquifers is critical for the successful implementation of geological CO2 sequestration projects. Although volumetric methods provide quick and accurate estimates for the storage capacity, the storage efficiency factor (SEF) relies on empirical correlations or computationally expensive numerical simulations. To overcome these limitations, this study proposed a novel regression-based prediction approach for directly predicting SEF in vertical and horizontal well scenarios using simulation-derived data. Four regression models were developed: multi-linear regression (MLR), exponential (Exp), and response surface methodology models (RSM-1 and RSM-2). Among them, the RSM-2 models, which incorporate linear, interaction, and quadratic terms, demonstrated the highest accuracy, achieving mean absolute percentage errors of 1.1% and 3.4% for vertical and horizontal wells, respectively. Compared to prior empirical approaches, the RSM-2 model provided more precise and consistent predictions, with a narrower SEF distribution range and reduced uncertainty, enhancing its suitability for site screening and storage planning. The models were applied to two reservoirs in the Pohang Basin, Southeast Korea, and yielded storage capacity estimates within 5% of full-physics simulation results, confirming their validity and reliability. These models provide a cost-effective and time-efficient alternative to conventional simulations and are particularly advantageous in early-stage feasibility studies and large-scale CO2 storage assessments. Future work will expand these models to accommodate more complex reservoir conditions and enhance their generalizability across diverse geological settings.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".