Estimation of rock physics properties via full waveform inversion of vertical seismic profile data recorded by accelerometer and fibre-optic sensors
Bibliographic record
Abstract
Combining elastic full waveform inversion (FWI) with rock physics holds promise for expanding the application of FWI beyond seismic imaging to predicting and monitoring reservoir properties. Distributed acoustic sensing (DAS), a rapidly developing seismic acquisition technology, is being explored for its potential in supporting FWI applications. In this study, we implement a sequential inversion scheme that integrates elastic FWI and Bayesian rock physics inversion, using a vertical seismic profile (VSP) data set acquired with accelerometer and collocated DAS fibre at the Carbon Management Canada's Newell County Facility. Our aim is to establish a baseline model of porosity and lithology parameters to support later monitoring of CO2 storage. Key strategies include an effective source approach for addressing near-surface complications, a modelling strategy to simulate DAS data comparable to field data, and a Gaussian mixture approach to capture the bimodality of rock properties. We conduct FWI tests on accelerometer, DAS, and combined accelerometer-DAS data. While our inversion results accurately reproduce either data set, the elastic models inverted from accelerometer data outperform the other two in matching well logs and identifying the target reservoir. We attribute this outcome to the limited complementarity of DAS data with accelerometer data in our experiment, along with the limitations imposed by single-component measurements on DAS. The porosity and lithology models predicted from accelerometer-derived elastic models are reasonably accurate at the well location and exhibit geologically meaningful spatial distribution.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".