Model-based Assessment of Seismic Monitoring of CO2 in a CCS Project in Alberta, Canada, Including a Poroelastic Approach
Bibliographic record
Abstract
The theoretical detectability of CO2 is investigated for the Quest Carbon Capture and Storage (CCS) project in Alberta, Canada. This is completed by using Gassmann fluid substitution and elastic seismic modeling. The study indicates that for the period of one year injection, the location and spatial distribution of the CO2 plume could be detected in the seismic data. This is providing the data have good bandwidth and a high signal to noise ratio. However, pore fluid properties such as density, velocity, viscosity, and saturation are neglected in elastic modeling in spite of the reservoir rocks being porous media saturated with fluids. Another phase of the study includes developing a more accurate forward modeling algorithm that takes the fluid properties into account. For this purpose, the Biot's equations of motion in poroelastic media are employed to develop a finite difference algorithm to simulate the wave propagation in poroelastic media. To examine the algorithm, a numerical example is defined based on the Quest project. The results reveal that the algorithm properly handles the layered models and thus can be used in the future to examine more complex models. This approach may also be better than elastic modeling for theoretical monitoring of CO2 since the reservoir fluid content is considered in the modeling process.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".