Joint AVO Inversion for Time-Lapse Elastic Reservoir Properties
Bibliographic record
Abstract
Abstract For time-lapse seismic inversion, it is common that baseline and monitor survey data are separately inverted to elastic properties. Elastic property changes are obtained from difference in the two inversion results. Buland and Quair (2006) proposed novel time-lapse inversion method based on the Bayesian theorem, in which posterior distribution of elastic property change are obtained from seismic data difference between two surveys along with the prior information. However, individual elastic properties at baseline (or repeat) survey cannot be simultaneously obtained. Moreover, they assumed constant wavelet although individual wavelets are commonly used in different vintage data. Therefore, to overcome the limitations, we have developed a new time-lapse seismic inversion method. The method uses both baseline and monitor survey data, instead of only uses the difference, and simultaneously obtains elastic properties (e.g., P- and S-wave velocities and density) at baseline survey and the changes at monitor survey as well as the uncertainties. Furthermore, the developed method allows us to use individual wavelet in each seismic data, resulting in multicomponent data which can be used. The method is applied to Hangingstone oilfield in Canada, where heavy-oil with 8.5 °API has been produced by SAGD method and time-lapse seismic data were acquired; baseline survey in 2002 and repeat survey in 2006. Both P-P and P-S data are available in the monitor survey while only P-P data is available in the baseline survey. We use all the available seismic data to obtain initial elastic properties at the baseline survey, which reasonably well agree with well log data, as well as the elastic property change due to steam injection. Furthermore, the elastic property change is converted to temperature change within reservoir by using heavy-oil rock physics model, which are reasonably consistent with measurement at observation 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".