Analytical Model for Leakage Detection in CO<sub>2</sub> Sequestration in Deep Saline Aquifers: Application to ex Situ and in Situ CO<sub>2</sub> Sequestration Processes
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide One of the prominent methods for carbon dioxide sequestration is disposal into deep saline aquifers. This is mainly because deep saline aquifers provide significant capacity for storage of unwanted fluids underground for a long period. However, saline aquifers may have a leaky cap rock. The sealing capacity of a cap rock must, therefore, be evaluated to ensure the integrity and safety of its storage media; hence robust classifications of the cap rock are required even before starting the storage/disposal operations. Aqueous fluids can be injected into a target storage aquifer, and pressure changes owing to leakage can be monitored in an upper aquifer separated by a cap rock for a short period. The measurement of pressure responses in the monitoring aquifer can be used to identify and characterize any leakage path in the cap rock. This paper provides analytical models in the Laplace domain for both in situ and ex situ CO 2 sequestration methods. Using the numerical Laplace inverse method called the “Stehfest” method, the analytical solution in the real domain is calculated. The analytical solutions developed can be used for determining both dimensionless pressure changes in monitoring and storage aquifers due to leakages and dimensionless leakage rates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".