Locating and quantifying greenhouse gas emissions at a geological CO<sub>2</sub>storage site using atmospheric modeling and measurements
Bibliographic record
Abstract
The Cooperative Research Centre for Greenhouse Gas Technologies (CO2CRC) Otway Project is Australia's first demonstration of the geological storage of carbon dioxide (CO2), where about 65,000 metric tons of fluid consisting of 92% CO2 and 8% methane (CH4) by mass have been injected underground. As part of the project objective of developing methodologies to detect, locate, and quantify potential leakage of the stored fluid into the atmosphere, we formulate an inverse atmospheric model based on a Bayesian probabilistic framework coupled to a state-of-the-art backward Lagrangian particle dispersion model. A Markov chain Monte Carlo method is used for efficiently sampling the posterior probability distribution of the source parameters. Controlled experiments used to test the model involved releases of the injected fluid from one of the nearby wells and were staggered over 1 month. Atmospheric measurements of CO2 and CH4 concentrations were taken at two stations installed in an upwind-downwind configuration. Modeling both the emission rate and the source location using the concentration measurements from only two stations is difficult, but the fact that the emission rate was constant, which is not an unrealistic scenario for potential geological leakage, allows us to compute both parameters. The modeled source parameters compare reasonably well with the actual values, with the CH4 tracer constraining the source better than CO2, largely as a result of its 6 times higher signal-to-noise ratio. The results lend confidence in the ability of atmospheric techniques to quantify potential leakage from CO2 storage as well as other source types.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".