Time-lapse numerical modeling of the Quest carbon capture and storage (CCS) project: Poroelastic approach
Bibliographic record
Abstract
Summary A finite-difference algorithm was developed based on the Biot’s equations of motion for modelling wave propagation in poroelastic media. In contrast with the elastic modelling, in the poroelastic approach the properties of the pore fluid are taken into account in the algorithm. Poroelastic modelling could be useful in cases where the fluid content of the rock is of interest, i.e. Carbon Capture and Storage (CCS) projects. We examined our program using a model based on the Quest CCS project in Alberta to investigate the detectability of CO2 after one year of injection. This was done by defining two models for the baseline and monitor scenarios that represented the subsurface before and after injecting CO2, respectively. The difference between the calculated seismic sections for the two scenarios shows that the residual amplitude is comparable with the signal amplitude. With this result, the injected CO2 in the Quest project over a year could be detected providing the data have good bandwidth and a high signal-to-noise ratio. Furthermore, a comparison between the poroelastic algorithm and the elastic algorithm shows that the time-lapse effect in the poroelastic case is smaller than the one in the elastic case. In the fluid saturated media some of the wave energy dissipates due to the traveling wave in the fluid and the poroelastic approach helps us to take this loss into account in the modeling process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".