A Practical Methodology For Integration of 4D Seismic in Steam-Assisted-Gravity-Drainage Reservoir Characterization
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Bibliographic record
Abstract
Summary 4D seismic is a dynamic source of data that provides information about changes in reservoir-rock and -fluid properties over time. Seismic attributes are sensitive to variations in the fluid content, temperature, and pressure distribution; therefore, 4D-seismic images contain information on the nature of fluid flow within the reservoir. Perhaps the most-reliable and -important information that can be learned from 4D-seismic images is related to anomalies in fluid flow within the reservoir. During steam-assisted gravity drainage (SAGD), the steam-chamber propagation is fairly clear from 4D-seismic images, mainly because of higher gas saturation in the chamber. Therefore, anomalies are revealed by the absence or unexpected location of the steam chamber. In this paper, a practical methodology is proposed for consideration of anomalies identified from 4D-seismic images in geostatistical reservoir models. The geostatistical realizations are updated to enforce missing anomalies and improve reservoir characterization. The updated models are suitable for reservoir decision making and management.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it