Quantitative FWI characterization of reservoir properties at the CMC Newell County Facility
Bibliographic record
Abstract
We apply a sequential inversion scheme combining elastic FWI and Bayesian rock physics inversion to a VSP dataset acquired with accelerometers and collocated DAS fiber at the Carbon Management Canada’s Newell County Facility. The goal is to build a baseline model of porosity and lithology parameters to support later monitoring of CO2 storage. The key strategies include an effective source approach to cope with near-surface complications, a modeling strategy to simulate DAS data directly comparable to the field data, and a Gaussian mixture approach to capture the bimodality of rock properties. We perform FWI tests on the accelerometer, DAS, and combined accelerometer-DAS data. While the results can accurately reproduce either type of data, the elastic models from the accelerator data outperform the other two in matching well logs and identifying the target reservoir. We attribute this result to the insignificant advantage of DAS data, in this case, over accelerometer data, which also suffers from single-component measurements and lower signal-to-noise ratios. The porosity and lithology models predicted from the accelerometer elastic models are reasonably accurate at the well location and are geologically meaningful in spatial distribution.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".