Design Considerations to Test Sealing Capacity of Saline Aquifers
Bibliographic record
Abstract
Abstract The geological storage of carbon dioxide (CO2) provides the possibility of maintaining access to fossil energy, while reducing emissions of CO2 to the atmosphere. One of the essential concerns in geologic storage is the risk of CO2 leakage from the storage formations. The leakage occurs through possible pathways in the seal. Characterization of the CO2 leakage pathways from the storage formations into overlying formations is required. The aquifer cap-rock may be characterized before CO2 storage. This will allow for the determination of proper storage aquifers and locations for the injection wells. In a companion paper, a flow and pressure test has been suggested for characterization of leakage pathways in aquifer cap-rock. Water is injected in the target aquifer, and the pressure is observed in an overlying aquifer. The pressure data are analyzed to characterize the leakage pathways in the cap-rock. In this work, design considerations to maximize the capability of leakage characterization are presented. A leakage pathway can be characterized by the leak transmissibility and location parameters. A successful test should be able to provide sufficient information to evaluate the leakage parameters. In this work, different strategies are evaluated in order to achieve a successful test. The strategies include increasing the sampling frequency, use of pulsing, increasing the number of monitoring/injection wells and utilization of prior information. Prior information on the leak is provided through analysis of the pressure derivative curve. Estimation of the leakage parameters is actually an inverse problem that is generally ill-conditioned and very sensitive to noise. The information provided by different strategies is evaluated, based on their effects on well-posing the inverse problem. The effects are studied based on information and correlation matrices, as well as the confidence interval.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 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 teacher head, 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".