Modeling of CO2 Leakage from CCS into Overlying Formations - Quest CCS Monitoring Evaluation
Bibliographic record
Abstract
Abstract Commercial-scale deployment of Carbon Capture and Storage (CCS) as a viable greenhouse gas emissions reduction technology requires that the CO2 be confidently contained in geological formations with no risk of groundwater contamination. To ensure containment is adequately monitored, an evaluation of what a potential leak could look like and how it can be detected is required. This paper is an example from the Quest CCS operation in Alberta, Canada. Quest commenced operation in August of 2015 at a rate of 1 MT/year into two injection wells. After two years of operations, the project’s rigorous monitoring program has demonstrated that the reservoir is behaving as expected and no leaks have been detected. However, hypothetical leak paths have been investigated and modeled. Four scales of models have been used to evaluate the risks associated with the hypothetical leak paths and therefore on containment: (1) Geological structural models - Regional Static Model, Field Dynamic Model, (2) Legacy well - Well Brine Leak Path Model (3) CO2 Leakage - Injection Well CO2 Leak Path Model, and (4) Leak detection - Cooking Lake Model. The results of the modelling were used in the evaluation of the Quest project proposal, the current operating strategy, and the measurement, monitoring, and verification (MMV) plan. The results of the leak path models demonstrate that the risk of a CO2 leakage from the Quest storage operation is very low. Regional modeling of the overburden confirmed that no leak pathways in the project area could be identified. Field level dynamic modelling demonstrated that injected CO2 is not expected to reach far field legacy wells, but the potential for elevated reservoir pressure displacing saline brine into usable ground water could be a risk if insufficient well count. The impact of a brine leak path was modeled and concluded to be negligible as the overlying under pressured cooking lake formation was concluded to be an effective pressure sink. As the injection wells have the highest pressures and concentrations of CO2 in the reservoir, despite excellent wellbore integrity, they are the most likely location for a theoretical CO2 leak path. It was both concluded that the buoyancy force was a very slow moving affect and that the cooking lake formation ultimately acts as a pressure sink. Therefore, the cooking lake was modeled to understand what pressure response could be expected and whether a leak into those formations could be detected. It was concluded that material leaks at the injection well would be differentiable from baseline pressure drift at the monitoring wells.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".