Integration of 4D Seismic in Steam-Assisted-Gravity-Drainage Reservoir Characterization
Bibliographic record
Abstract
Summary 4D seismic is one of the main sources of dynamic data for heavy-oil-reservoir monitoring and management. 4D seismic is significant because seismic attributes such as velocity and impedance depend on variations in reservoir-fluid content, temperature, and pressure distribution as a result of hydrocarbon production. Thus, the large-scale nature of fluid flow within the reservoir can be evaluated through information provided by 4D-seismic data. Such information may be described as anomalies in fluid flow that can be inferred from the unusual patterns in variations of a seismic attribute. During steam-assisted gravity drainage (SAGD), the steam-chamber propagation is fairly clear from 4D-seismic data mainly because of changes in reservoir conditions caused by steam injection and bitumen production. Anomalies in the propagation of the steam chamber reflect the quality of fluid flow within the reservoir. A practical methodology is implemented for integration of 4D seismic into SAGD reservoir characterization for the Surmont project.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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".