Leakage detection and characterization through pressure monitoring
Bibliographic record
Abstract
Characterization of the CO2 leakage pathways from the storage formations into overlying formations is required. We present a flow and pressure test to locate and characterize the leaks. The flow test is based on the injection (or production) of water into (or from) a storage aquifer at a constant rate. The pressure is measured at one or several monitoring wells in an aquifer overlying the storage aquifer, which is separated by an aquitard. The objective of the test is to locate and characterize any leakage through the separating aquitard. We present an inverse procedure to obtain the leakage pathway transmissibility and location, based on the pressure measurements in the presence of noise. A single monitoring well allows good determination of the leak magnitude but provides limited constraints on location. Adding a second monitoring well provides two-dimensional location of the leak location in the presence of noise/uncertainty in pressure measurements. It seems plausible that the use of multiple monitoring wells could enable cost-effective and sensitive detection of leakage over a large area. Unlike seismic imaging which only detects leakage when CO2 penetrates the leak, these methods are able to test for leaks before CO2 injection, or during injection but before the CO2 plume reaches the leak.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".