Towards the early detection of CO2 leaks from carbon storage sites: modelling and measurement of reaction, diffusion and mass flow of CO2 and O2 in near-surface soils
Bibliographic record
Abstract
Carbon capture and storage (CCS) of CO2 in geological reservoirs is being used to mitigate climate change and so a method and technology is needed for early leak detection. This thesis focuses on understanding the reaction, diffusion, and mass flows of O2 and CO2 in near surface soils with or without a CO2 leak. Two numerical models were developed to describe O2 and CO2 reactions, diffusion, mass flow and concentration gradients in the top 1 m of Alberta soils. The first model showed that under typical soil pH values, virtually all CO2 and O2 diffusion would occur in the gaseous phase, with less than 0.10% occurring in the aqueous phase, even for high soil water content. The second model calculated the contribution of diffusion and mass flow for CO2 and O2 in the gas phase of soils having a range of respiratory quotient (RQ=CO2 flux/O2 flux) values, with or without a CO2 leak. With RQ values ranging from 0.7 to 1.2, mass flow was predicted to account for -0.19% to 1.09% of CO2 flux to the soil surface, respectively. When simulated rates of CO2 leakage were set at 1 and 5 times the net biological flux from soils, the contribution of mass flow to total CO2 flux increased to about 3% and 13%, respectively. The model was also used to identify the Gas Concentration Ratio (GCR = [CO2] differential between bulk air and soil surface / [O2] differential between bulk air and soil surface) as a metric that could be used to identify soils impacted by a CO2 leak. Three gas analysis systems were built for use with a soil column to test the accuracy of the models and the potential value of a GCR measurement. The observed concentration gradients were found to be a good fit to the model predictions and the observed GCR measurements were able to differentiate between soils impacted by CO2 leaks as low as 2 to 3 times the normal biological flux rate of soils (ca. 2 μmolCO2/m2/s). This work supports the further development and use of a portable instrument to carry out GCR measurements at CCS sites.
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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 | 0.000 |
| 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".